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\begin{document}

\title{Computational Design of Rasagiline Derivatives: Searching for Enhanced
Antioxidant Capability}



\author[1]{Miguel Reina}%
\author[2]{Eduardo  Guzman-Lopez}%
\author[3]{Annia Galano}%
\affil[1]{Instituto de Investigaciones en Materiales}%
\affil[2]{Universidad Autonoma Metropolitana Iztapalapa Division de Ciencias Basicas e Ingenieria}%
\affil[3]{Universidad Autonoma Metropolitana-Iztapalapa}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
A set of new rasagiline derivatives is presented. They were designed to
be antioxidant compounds with the potential to be used for treating
neurodegenerative disorders. They are expected to be multifunctional
molecules that can help reduce oxidative stress, which is thought to
contribute to neurodegenerative disorders. The CADMA-Chem computational
protocol was used to produce rasagiline derivatives and to evaluate
their likeliness as oral drugs and antioxidants. Three of them were
identified as the most promising ones. They are proposed to be better
free radical scavengers than rasagiline. In addition, they are expected
to keep the parent's molecule neuroprotective capability. Hopefully, the
results presented here would promote further experimental and
theoretical investigations on these compounds.%
\end{abstract}%



\sloppy


\textbf{Computational Design of Rasagiline Derivatives: Searching for
Enhanced Antioxidant Capability}

Miguel Reina,\textsuperscript{1,2} Eduardo Gabriel
Guzm\selectlanguage{ngerman}án-López\textsuperscript{1} and Annia
Galano11\textsuperscript{*}E-mail: agalano@prodigy.net.mx,
agal@xanum.uam.mx\textsuperscript{1}

\emph{\textsuperscript{1}Departamento de Química. Universidad Autónoma
Metropolitana-Iztapalapa. San Rafael Atlixco 186, Col. Vicentina.
Iztapalapa. C. P. 09340. México D. F. México.}

\emph{2Departamento de Química Inorgánica y Nuclear, Facultad de
Química, Universidad Nacional Autónoma de México, México DF 04510,
Mexico.}

\textbf{Abstract}

A set of new rasagiline derivatives is presented. They were designed to
be antioxidant compounds with the potential to be used for treating
neurodegenerative disorders. They are expected to be multifunctional
molecules that can help reduce oxidative stress, which is thought to
contribute to neurodegenerative disorders. The CADMA-Chem computational
protocol was used to produce rasagiline derivatives and to evaluate
their likeliness as oral drugs and antioxidants. Three of them were
identified as the most promising ones. They are proposed to be better
free radical scavengers than rasagiline. In addition, they are expected
to keep the parent's molecule neuroprotective capability. Hopefully, the
results presented here would promote further experimental and
theoretical investigations on these compounds.

\textbf{Keywords} : rasagiline; 1-(R)-aminoindan; DFT; ADME;
computer-aided design, free radical scavenger, oxidative stress;
toxicity; synthetic accessibility.



\textbf{Introduction}

Neurodegenerative disorders (ND); including
Parkinson's\textsuperscript{1, 2}, Alzheimer's,\textsuperscript{3, 4}
and other neural diseases;\textsuperscript{5} have been described to
involve the gradual loss of particular neuronal cell population.
Although numerous investigations have been devoted to the elucidation of
the ND pathologies, their primary causes continue elusive. Some
interrelated crucial aspects on the progression of these disorders are
proteopathy\textsuperscript{6-8}, metal ion
dyshomeostasis\textsuperscript{9-32}, environmental
pollutants,\textsuperscript{33-47} and neurotransmitter deficiencies. A
chemical phenomenom shared by all these diseases is oxidative stress
(OS). It could be accountable for the dysfunction or death of neuronal
cells that conduces to disease pathogenesis.\textsuperscript{48-65}

OS arises due to unregulated production and consumption of free
radicals, primarily reactive oxygen species
(ROS).\textsuperscript{66-78} ROS are produced from molecular oxygen in
the mitochondrial respiratory chain. Some partially reduced
O\textsubscript{2} intermediates are formed in low amounts, including
the highly reactive hydroxyl radical (\selectlanguage{english}\textsuperscript{*}OH) and the
superoxide anion (O\textsubscript{2}\selectlanguage{english}\textsuperscript{*-}), among
others\textsuperscript{66, 74, 75, 78-84}. Neurons are particularly
vulnerable to ROS-induced damage because of their high oxygen
consumption, relatively low antioxidant defense, low regenerative
capacity, and high polyunsaturated fatty acid
content.\textsuperscript{78, 85-87} Thus, ROS overproduction in brain
tissue imposes a very harmful threat. One possible therapeutic strategy
is to prevent and/or diminish OS using free radical scavengers. In fact,
a variety of neuroprotective drug agents are used because of their
antioxidant capability\textsuperscript{88-97}.

Rasagiline (N-propargyl-1-(R)-aminoindan; Azilect\textsuperscript{(r)}) is
an anti-Parkinson, selective irreversible monoamine oxidase (MAO-B)
inhibitor drug. It is currently accepted by the US Food and Drug
Administration, and has demonstrated to show neuroprotective and
neurorestorative activities in \emph{in vitro} and \emph{in}
\emph{vivo}models\textsuperscript{98-114}. Rasagiline benefits patients
with both early and late Parkinson's disease as monotherapy or as an
assistant to levodopa.\textsuperscript{115-117} From a chemical point of
view, rasagiline is a secondary cyclic benzylamine and indane
derivative, in which the acetylenic group provides hydrophobicity and
steric volume.

Rasagiline can act as a hydrogen bond donor, enabling an additional
interaction with monoamine oxidase B enzyme. Youdim et al. reported that
the S-isomer of rasagiline, TVP1022, is thousands times less potent as a
MAO-B inhibitor\textsuperscript{118-120} than selegiline. Nevertheless,
both compounds present similar molecular mechanisms, suggesting that the
neuroprotective effect of rasagiline is not depending on MAO-B
inhibition, but to some extent is related to the N-propargyl moiety.

Rasagiline is mainly metabolized by hepatic cytochrome P-450. Its major
metabolite, 1-(R)-aminoindan, is a weak reversible MAO-B inhibitor and a
non-amphetamine compound with antioxidant and neuroprotective
capabilities\textsuperscript{121}. 1-(R)-aminoindan has reversed
behavioral asymmetry and restored striatal catecholamine levels and
neurons protection from hydrogen peroxide-induced oxidative
stress\emph{in vivo} models of Parkinson's disease.\textsuperscript{122,
123}5-hydroxy-1-(R)-aminoindan is the major metabolite of Ladostigil
{[}(N-propargyl-(3R) aminoindan-5yl)-ethyl methyl carbamate{]}, an
anti-Parkinson drug. It \emph{in} \emph{vitro} neuroprotective
capabilities are similar to those of 1-(R)-aminoindan. These findings
suggest that 1-(R)-aminoindan and 5-hydroxy-1-(R)-aminoindan contribute
to the overall neuroprotective activity of its parental compounds and
are also neuro-active compounds.\textsuperscript{124, 125} So, is the
N-propargyl moiety a key feature to design novel derivatives with potent
antioxidant behavior?

Based on the above-discussed information, the aims of this study are:

-To performe a rational search of molecules derived from the rasagiline
framework, using a computer-assisted design protocol.

-To analyze the importance of the rasagiline triple bond and of the
1-(R)-aminoindan moiety in their drug-like behaviour and antioxidant
potential.

-To propose rasagiline derivatives with high probabilities of being
multipurpose antioxidants capable of lowering OS.

To accomplish that, the rasagiline structure was systematically modified
in two different ways: (i) By inserting different functional groups in
all the possible positions of the aromatic ring. (ii) By replacing the
triple bond of rasagiline with a methyl group or a hydrogen atom (Scheme
1).

The reported results are expected to encourage further theoretical and
experimental investigations on these molecules.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Scheme 1} . Structure of compounds under study (rasagiline (R),
N-(propanyl)-2,3-dihydroinden-1-amine (R\textsubscript{I}) and
1-(R)-aminoindan (R\textsubscript{II}).

\textbf{Computational details}

Numerous physicochemical parameters for absorption, distribution,
metabolism and excretion (ADME) were evaluated for all the designed R,
R\textsubscript{I} and R\textsubscript{II} frameworks employing
Molinspiration Property Calculation Service\textsuperscript{126} and
DruLiTo software.\textsuperscript{127} The computed parameters were
employed to confirm if the designed derivatives satisfy the Lipinski's
rule of five, the Ghose's rule, and the Veber
criteria.\textsuperscript{128-131} Compounds violating more than one of
Lipinski's rules might have difficulties with bioavailability. Those
violating Ghose's rules could show absorption problems or low
permeation; and those following Veber criteria may have better chances
of suitable oral bioavailability.

Since all these conditions are general rules and not rigorous
regulations, viable drugs have also to fulfill other vital requirements,
such as synthetic accessibility (SA) and
safety.\textsuperscript{132-135} The SA of the designed compounds was
determined with the SYLVIA-XT 1.4 program (Molecular Networks, Erlangen,
Germany).\textsuperscript{136} It delivers a value between 1 and 10. The
smaller the value the easier to synthesize is the compound. The SYLVIA
program has been certified for ranking virtual compounds during drug
discovery processes\textsuperscript{137}. Additionally,
LD\textsubscript{50} and Ames mutagenicity (M) were employed in this
work to assess the toxicity of R, R\textsubscript{I} and
R\textsubscript{II} and its derivatives. The Toxicity Estimation
Software Tool (T.E.S.T.), version 4.1, was employed to obtain these
parameters.\textsuperscript{138} This software constructs predictions
based on quantitative structure-activity relationships (QSAR), which are
envisioned for screening new compounds. The LD\textsubscript{50} and M
descriptors were determined with the consensus method, which makes
predictions as the average of the toxicities obtained with several QSAR
methodologies.\textsuperscript{139} There is a general understanding
that the consensus method commonly offers higher accuracy and coverage
than other protocols. Selection and elimination scores, expressed in
terms of toxicity, manufacturability and ADME properties were used to
make the first selection of derivatives.\textsuperscript{140-142}

Gaussian 09 package was employed for electronic structure
calculations\textsuperscript{143}. Geometry optimizations and frequency
calculations were carried out using the Density Functional Theory (DFT).
The M05-2X approach was used in conjunction with 6-311+G(d,p) basis set
and the solvation model density (SMD) using water to mimic a polar
environment.\textsuperscript{144} Local minima were identified by the
absence of imaginary frequencies. Unrestricted calculations were used
for open shell systems. M05-2X is a global hybrid exchange-correlation
general gradient approximations functional designed for noncovalent
interactions, kinetics and thermochemistry.\textsuperscript{145} It has
also been recommended for calculating reaction energies involving free
radicals\textsuperscript{146}. Furthermore, the M05-2X functional has
been widely used for estimating pKa values, bonding dissociation
energies, and the free radical scavenging activity of numerous
antioxidant molecules\textsuperscript{147-151}.

Ionization energies (IE) and electron affinities (EA) were calculated in
the framework of the electron propagator theory
(EPT),\textsuperscript{152, 153} which usually delivers values closer to
those derived from experimental results than other methodologies. The
partial third-order quasiparticle theory (P3)\textsuperscript{154} was
chosen since it has been reported to have lower mean errors, compared to
other methods.\textsuperscript{155} However, for the EPT approximations
(including P3) to be valid, pole strength (PS) values are needed to be
larger than 0.80-0.85 \textsuperscript{156} Electrophilicity, \selectlanguage{greek}ω, \selectlanguage{english}was
also estimated for electron transfer
reactions\textsuperscript{157-159}to analyze the chemical behavior of
the designed R, R\textsubscript{I}and R\textsubscript{II} derivatives.
In a chemical reaction, involving two molecules, that with the higher \selectlanguage{greek}ω
\selectlanguage{english}is expected to act as the electrophile, while the other will behave as
the nucleophile. This index was calculated following the equation:

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/492535/articles/575244-computational-design-of-rasagiline-derivatives-searching-for-enhanced-antioxidant-capability}

For the computation of bond dissociation energies (BDE), all sites that
are likely to act as H donors were considered (Scheme 2). They
correspond to those already present in the rasagiline framework
(sites\emph{a} , \emph{b} , \emph{c} and \emph{d} ), and the new
possibilities arising from incorporating functional groups (-OH,
-NH\textsubscript{2}, -SH and -COOH) in sites R\textsubscript{1} to
R\textsubscript{4}. These four groups were chosen because they can
enhance the antioxidant behaviour and modify the acid-base ratio. Two
rasagiline analogs were used to analyze the effects of the terminal
alkyne group in physicochemical parameters, toxicity, synthetic
accessibility, and the global reactivity indexes. They are:
N-(propanyl)-2,3-dihydroinden-1-amine (R\textsubscript{I}), which
present a terminal methyl group instead of the corresponding rasagiline
terminal alkyne; and 1-(R)-aminoindan (R\textsubscript{II}) that present
no alkyl chain. The derived comparisons could be interesting since no
rational studies comparing these molecules have been carried out.
Because of this structural modification, the \emph{e} site in
R\textsubscript{I} was included, while \emph{d} site in
R\textsubscript{II} was not considered, for BDE calculations.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Scheme 2} . Sites considered in the BDE calculations for R,
R\textsubscript{I} and R\textsubscript{II}.

The proportion of neutral vs. charged species for molecules with
acid-base bahaviour is ruled by the pKa-pH relationship. Thus, acid
constants, expressed as pKa, were calculated for the subset of the most
promising R, R\textsubscript{I} and R\textsubscript{II} derivatives. A
fitted parameters approach (FPA) was used for that, because it was
already proven as reliable.\textsuperscript{160, 161} The calculated
data was compared with a reference set of molecules, previously used for
similar purposes.\textsuperscript{142} The computational protocol
described here is known as CADMA-Chem.\textsuperscript{140}

\textbf{Results and discussion}

The data on the compounds used as references are reported in Table S1
and Table S2, Supporting Information. The data on the designed
rasagiline derivatives are provided in Table S3, and their estimated
properties in Table S4.

Three central matters were addressed in this investigation:

\begin{itemize}
\tightlist
\item
  building the candidate molecules,
\item
  sampling the search space in a suitable maner, and
\item
  assessing their potential for the intended purpose.
\end{itemize}

Following the CADMA-Chem protocol, above-explained; -OH,
-NH\textsubscript{2}, -SH and -COOH functional groups were inserted in
sites R\textsubscript{1} to R\textsubscript{4} of R,
R\textsubscript{I}and R\textsubscript{II,} This led to 361 rasagiline
derivatives. and are considered as the first two rasagiline derivatives,
48 compounds have only one functional group insertion (all possible
species within the used scheme), 288 compounds have two functional
groups substituted using any possible combination, and 23 have three
added functional groups. The latter were built from the most promising
bi-functionalized species.

ADME properties, toxicity values, and synthetic accessibility (Table S4)
are used in the selection score (S\textsuperscript{S}), as previously
described\textsuperscript{140-142}. The higher the value of
S\textsuperscript{S} (Table S5) the more probable that a rasagiline
derivative presents a drug-like behavior. Equations on how
S\textsuperscript{S} has been constructed considering ADME (absorption,
distribution, metabolism, excretion) properties, toxicity (T), and
synthetic accessibility (SA) can be found in Appendix S1.

Figure 1 presents the selection score (S\textsuperscript{S}) of
rasagiline derivatives designed in this work. The values of individual
properties used to assess S\textsuperscript{S} are presented in Table
S4, while the reference values to compare with are those reported in
Tables S1 and S2. In general, the molecules with higher
S\textsuperscript{S} values are likely to have lower toxicity, improved
synthetic accessibility, and enhanced ADME properties. Based on this
criterion, sixteen rasagiline derivatives (Scheme 3) were chosen for the
next stage, i.e., to determine their potential as antioxidants based on
electronic structure calculations. Additionally, even though
1-(R)-aminoindan does not present a selection score value high enough to
belong to the subset group, it has been included for comparison purposes
on its own subfamily of derivatives.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 1} . Selection score (S\textsuperscript{S}) for the
rasagiline derivatives designed in this work. Vertical lines mark the
arithmetic mean of the reference set (red) and the value for the parent
molecule (rasagiline, green).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Scheme 3} . Structure and S\textsuperscript{S} values of
rasagiline and the derivatives selected for the next stage of the
investigation.

To verify if any molecule in the selected subset considerably diverges
(in any of its properties) from the average value of the reference
set,\textsuperscript{140-142} four exclusion scores
(S\textsuperscript{E}) were also evaluated (S\textsuperscript{E,ADME2},
S\textsuperscript{E,ADME8}, S\textsuperscript{E,ADMET} and
S\textsuperscript{E,ADMETSA}). Their equations can be found in Appendix
S2 of Supporting Information. S\textsuperscript{E,ADME2} is the same to
that formerly reported,\textsuperscript{162} and the others use the same
kind of scrutiny but including 8 terms for ADME properties, two terms
related to toxicity (T), and a term for synthetic accessibility (SA).
Thus, the accronyms are self-explanatory. These exclusion scores (Figure
2 and Table S6) measure deviations from the average values of already
used oral drugs.

S\textsuperscript{E,ADME2} values have been described to be between 1.2
and 1.5 when studying 1791, 152 and 35 oral drugs,
respectively.\textsuperscript{140-142, 162} On the other hand, for the
rasagiline derivatives with the highest selection scores, average
S\textsuperscript{E,ADME2} =1.35, with individual values ranging from
0.6 to 2.9 for the whole set (Table S6, Supporting Information). The
average S\textsuperscript{E,ADME8} is 5.9, with individual values
ranging from 2.4 to 9.9; the average S\textsuperscript{E,ADMET} was
found to be 7.6, with individual values ranging from 3.7 to 12.7; and
the average S\textsuperscript{E,ADMETSA} is 7.8, with individual values
ranging from 3.8 to 13.3 (Table S6). It is essential to keep in mind,
though, that high values of these scores may arise from either worse or
better behavior (as oral drug-like species) than the average of the
reference set of drugs.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 2} . Elimination score (S\textsuperscript{E}) for the
most promising rasagiline derivatives, according to
S\textsuperscript{S}. Columns are divided to show the influence of the
new contributions included in each score, with respect to the previous
one.

Figure 2 shows that the new properties included in
S\textsuperscript{E,ADME8}, and the toxicity indexes have the largest
contribution to S\textsuperscript{E,ADMETSA}. Thus, the proposed index
emphasizes the importance of toxicity in the choice of candidates drugs.
Figure 3 shows a more meticulous analysis of the individual
contributions of the different parameters to the
S\textsuperscript{E,ADMETSA} elimination score.

The largest deviations arise mainly from LD\textsubscript{50}, M, PSA,
MW, HB\textsuperscript{A}, and HB\textsuperscript{D}.

Regarding LD\textsubscript{50}, the rasagiline derivatives deviating the
most from the average value (R-8, R\textsubscript{I}-36 and
R\textsubscript{II}-2) are less toxic to rats than the reference
compounds (LD\textsubscript{50} = 960.8)\textsuperscript{140-142}. Their
LD\textsubscript{50} were estimated to be 1970.2, 1613.9, 1882.0, and
2644.1, respectively. Thus, these important deviations imply a more
desirable behaviour, compared to the reference set. Therefore, these
derivatives were included in the subset selected as the most promising,
based on ADMETSA properties. A similar trend was found for the Ames
mutagenicity, i.e., the compounds predicted as the least mutagenic are
just those that deviate the most from the reference set (M =
0.41)\textsuperscript{140-142}. They are R\textsubscript{I},
R\textsubscript{I}-4, R\textsubscript{I}-36, R\textsubscript{I}-49,
R\textsubscript{I}-97, R\textsubscript{II}-2, and R\textsubscript{II}-10
all with M [?] 0.03. Consequently, it is essential not only to detect the
designed molecules with the largest deviation from the reference set,
but also to examine what causes such deviations. Otherwise, promising
candidates could be excluded for no good reason.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}

\textbf{Figure 3} . Individual contributions to the elimination score
(S\textsuperscript{E}), for the most promising rasagiline derivatives.

Contrarily, for the other indexes (PSA, HB\textsuperscript{A} and
HB\textsuperscript{D}) larger S\textsuperscript{E} values denote that
the properties of the examined derivatives deviate from the reference
average, although they still fulfill the Lipinski's and Ghose's rules,
as well as the Veber criteria. Regarding PSA, the chosen rasagiline
derivatives deviating the most from the reference set are dR-6, dR-7,
dR-8, and R\textsubscript{I}, but their PSA values (12.03 for the first
three and 26.02 \selectlanguage{ngerman}Å\textsuperscript{2} for the last) are below the Veber's
limit: 140 Å\textsuperscript{2}. The largest deviations for
HB\textsuperscript{A} and HB\textsuperscript{D} correspond to R-6, R-7,
R8 and R\textsubscript{I} with HB\textsuperscript{A} = 1 and to
R\textsubscript{I}-84 and R\textsubscript{II}-10, with
HB\textsuperscript{D} = 4, respectively. Once again, they do not
represent violations of the Lipinski's rule. Stand on what has been
considered, none of the 16 rasagiline derivatives selected as the most
promising candidates, following the selection score, was excluded after
the exhaustive screening using the elimination scores. Hence, IE, EA,
and electrophilicity were estimated and analyzed for all of them.

Since all the rasagiline derivatives selected in the subset may be
involved in acid-base equilibria, and such equilibria frequently
influences the antioxidant capability, their \emph{p} Ka values were
determined, as well as their molar fractions
(\textsuperscript{M}\emph{f} ) at physiological \emph{p} H (Table 1).
Their corresponding deprotonation routes and their distribution diagrams
were also elucidated (Scheme S1 and Figure S1 in the Supporting
Information). Ionization energies, electron affinities, and
electrophilicities for the acid-base species with non-negligible
population (\textsuperscript{M}\emph{f} \textgreater{} 0.1\%) at\emph{p}
H=7.4, are reported in Table 2.

\textbf{Table 1} . Estimated \emph{p} Ka values and molar fractions of
the diprotonated (\textsuperscript{M}\emph{f} \textsubscript{diprot}),
protonated (\textsuperscript{M}\emph{f} \textsubscript{prot}), neutral
(\textsuperscript{M}\emph{f} \textsubscript{neutral}), anionic
(\textsuperscript{M}\emph{f} \textsubscript{anion}) dianionic
(\textsuperscript{M}\emph{f} \textsubscript{dian}) and trianionic
(\textsuperscript{M}\emph{f} \textsubscript{trian}) species of
rasagiline its derivatives, at \emph{p} H=7.4.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& \emph{p}Ka\textsubscript{1} & \emph{p}Ka\textsubscript{2} &
\emph{p}Ka\textsubscript{3} & \emph{p}Ka\textsubscript{4} &
\textsuperscript{M}\emph{f}\textsubscript{diprot} &
\textsuperscript{M}\emph{f}\textsubscript{prot} &
\textsuperscript{M}\emph{f}\textsubscript{neutral} &
\textsuperscript{M}\emph{f}\textsubscript{anion} &
\emph{\textsuperscript{M}f}\textsubscript{dian}\tabularnewline
\midrule
\endhead
R & 7.49 & - & - & - & - & 0.552 & 0.448 & - & -\tabularnewline
dR-6 & 6.25 & 11.15 & - & - & - & 0.066 & 0.934 &
\textless{}10\textsuperscript{-3} & -\tabularnewline
dR-7 & 6.12 & 11.01 & - & - & - & 0.050 & 0.950 &
\textless{}10\textsuperscript{-3} &\tabularnewline
dR-8 & 2.79 & 11.06 & - & - & - & \textless{}10\textsuperscript{-4} &
\(\approx\)1.00 & \textless{}10\textsuperscript{-3}
&\tabularnewline
dR-103 & 0.98 & 5.80 & 7.92 & - & \textless{}10\textsuperscript{-8} &
0.019 & 0.754 & 0.228 & -\tabularnewline
dR-113 & 3.94 & 8.95 & 10.74 & - & - & \textless{}10\textsuperscript{-3}
& 0.972 & 0.027 & \textless{}10\textsuperscript{-4}\tabularnewline
dR\textsubscript{I} & 10.10 & - & - & - & - & 0.998 & 0.002 & - &
-\tabularnewline
dR\textsubscript{I}-4 & 8.05 & 13.04 & - & - & - & 0.817 & 0.183 &
\textless{}10\textsuperscript{-6} & -\tabularnewline
dR\textsubscript{I}-28 & 2.39 & 8.99 & 13.54 & - & - &
\textless{}10\textsuperscript{-5} & 0.975 & 0.025 &
\textless{}10\textsuperscript{-7}\tabularnewline
dR\textsubscript{I}-36 & 2.01 & 9.49 & 14.03 & - & - &
\textless{}10\textsuperscript{-5} & 0.992 & 0.008 &
\textless{}10\textsuperscript{-8}\tabularnewline
dR\textsubscript{I}-44 & 3.48 & 6.57 & 10.99 & - & - &
\textless{}10\textsuperscript{-4} & 0.129 & 0.871 &
\textless{}10\textsuperscript{-3}\tabularnewline
dR\textsubscript{I}-49 & 6.52 & 9.50 & 13.68 & - & - & 0.116 & 0.877 &
0.007 & \textless{}10\textsuperscript{-8}\tabularnewline
dR\textsubscript{I}-84 & 1.36 & 3.65 & 12.86 & - &
\textless{}10\textsuperscript{-9} & \textless{}10\textsuperscript{-3} &
\(\approx\)1.00 & \textless{}10\textsuperscript{-5} &
-\tabularnewline
dR\textsubscript{I}-97 & 4.25 & 7.45 & 13.19 & - &
\textless{}10\textsuperscript{-3} & 0.529 & 0.471 &
\textless{}10\textsuperscript{-6} & -\tabularnewline
dR\textsubscript{II} & 9.10 & - & - & - & - & 0.980 & 0.020 & - &
-\tabularnewline
dR\textsubscript{II}-2 & 9.33 & 10.26 & - & - & - & 0.988 & 0.012 &
\textless{}10\textsuperscript{-4} & -\tabularnewline
dR\textsubscript{II}-10 & 3.55 & 9.91 & - & - &
\textless{}10\textsuperscript{-3} & 0.997 & 0.003 & - & -\tabularnewline
dR\textsubscript{II}-22 & 5.86 & 7.97 & 10.78 & - & - & 0.022 & 0.770 &
0.207 & \textless{}10\textsuperscript{-4}\tabularnewline
\bottomrule
\end{longtable}

The purpose of presenting the electrophilicity (\selectlanguage{greek}ω\selectlanguage{english}) and the bonding
dissociation energies (Table 2) is to assist in the prediction of the
antioxidant behavior, via free radical scavenging chemical activity,
especially for by hydrogen atom transfer (HAT) and single electron
transfer (SET) mechanisms. Figure 4 presents the eH-DAMA (\emph{e}
lectron and \emph{h} ydrogen \emph{d} onating \emph{a} bility\emph{m} ap
for \emph{a} ntioxidants) for the rasagiline derivatives. This graphical
tool has been recently designed to simultaneously account for the
likeliness of molecules as H donors (formal HAT reactions) and electron
donors (SET reactions).\textsuperscript{82-84} It is constructed taking
into account both the bond dissociation energies (related to HAT
feasibility), and electrophilicity (related to SET viability). The pole
strength (PS) values, arising from the EPT calculations (used to obtain
electrophilicities) are all in the recommended range (Table S7). The
complete set of BDE values for each species is provided as Supporting
Information (Table S8). eH-DAMA employs electrophilicity (\selectlanguage{greek}ω\selectlanguage{english}) over IE,
since molecules with very low IE, are actually not expected to be very
effective as free radical scavengers acting as electron donors in SET
reactions since they are located in the inverted region of the Marcus
parabola. In those cases, electrophilicity (\selectlanguage{greek}ω\selectlanguage{english}) should be used instead of
IE (Figure S3 and S4).\textsuperscript{140-142}

As Figure 4 shows, the most promising acid-base species for deactivating
free radicals via SET are the anions. They have the lowest \selectlanguage{greek}ω \selectlanguage{english}values,
followed by the neutral and the protonated species, respectively. In
Figure 4, the parent molecule and Trolox are also included for
comparison purposes, and the
H\textsubscript{2}O\textsubscript{2}/O\textsubscript{2}\textsuperscript{*-}pair
as the potential oxidant target. The latter has been chosen because it
is usually harder to scavenge peroxyl radicals than other ROS. In the
eH-DAMA, the species with low BDE are likely to be especially competent
for scavenging free radicals acting as H donors via formal HAT, while
species with low \selectlanguage{greek}ω \selectlanguage{english}are expected to be efficient for scavenging free
radicals acting as electron donors via SET. Hence, the species located
at the bottom and left side of the eH-DAMA are assumed to act both ways.
Thus, they can be considered \emph{a priori} as good antioxidants.
dR-113 is likely the most promising antioxidant from rasagiline
derivatives followed by the neutral species of R\textsubscript{II}-22
and R\textsubscript{I}-49 which can be efficient as H donors but not
perform particularly well for electron transfer reactions.

\textbf{Table 2} . First ionization energy (IE, eV) and electron
affinities (EA, eV), electrophilicity (\selectlanguage{greek}ω \selectlanguage{english}and bond dissociation energies
(BDE, kcal/mol) for rasagiline and the selected subset of derivatives.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& & IE & IE & EA & EA & \selectlanguage{greek}ω & \selectlanguage{greek}ω & BDE & BDE\tabularnewline
\midrule
\endhead
Protonated & Protonated & & & & & & & &\tabularnewline
R & R & 12.36 & 12.36 & 2.69 & 2.69 & 2.93 & 2.93 & 86.92 &
86.92\tabularnewline
dR-6 & dR-6 & 11.08 & 11.08 & 2.72 & 2.72 & 2.85 & 2.85 & 77.82 &
77.82\tabularnewline
dR-7 & dR-7 & 11.08 & 11.08 & 2.72 & 2.72 & 2.85 & 2.85 & 76.62 &
76.62\tabularnewline
dR-103 & dR-103 & 11.36 & 11.36 & 3.31 & 3.31 & 3.34 & 3.34 & 84.88 &
84.88\tabularnewline
dR\textsubscript{I} & dR\textsubscript{I} & 12.26 & 12.26 & 2.48 & 2.48
& 2.77 & 2.77 & 86.86 & 86.86\tabularnewline
dR\textsubscript{I}-4 & dR\textsubscript{I}-4 & 11.93 & 11.93 & 2.19 &
2.19 & 2.56 & 2.56 & 86.83 & 86.83\tabularnewline
dR\textsubscript{I}-49 & dR\textsubscript{I}-49 & 10.85 & 10.85 & 2.26 &
2.26 & 2.5 & 2.5 & 74.73 & 74.73\tabularnewline
dR\textsubscript{I}-97 & dR\textsubscript{I}-97 & 12.2 & 12.2 & 2.35 &
2.35 & 2.68 & 2.68 & 86.38 & 86.38\tabularnewline
dR\textsubscript{II} & dR\textsubscript{II} & 12.52 & 12.52 & 3.28 &
3.28 & 3.38 & 3.38 & 86.34 & 86.34\tabularnewline
dR\textsubscript{II}-2 & dR\textsubscript{II}-2 & 11.99 & 11.99 & 3.28 &
3.28 & 3.34 & 3.34 & 86.34 & 86.34\tabularnewline
dR\textsubscript{II}-10 & dR\textsubscript{II}-10 & 11.18 & 11.18 & 3.14
& 3.14 & 3.19 & 3.19 & 86.21 & 86.21\tabularnewline
dR\textsubscript{II}-22 & dR\textsubscript{II}-22 & 11.24 & 11.24 & 3.29
& 3.29 & 3.32 & 3.32 & 67.39 & 67.39\tabularnewline
Neutral & Neutral & & & & & & & &\tabularnewline
R & R & 8.70 & 8.70 & -1.18 & -1.18 & 0.72 & 0.72 & 78.72 &
78.72\tabularnewline
dR-6 & dR-6 & 4.94 & 4.94 & 0.32 & 0.32 & 0.75 & 0.75 & 86.79 &
86.79\tabularnewline
dR-7 & dR-7 & 4.96 & 4.96 & 0.14 & 0.14 & 0.67 & 0.67 & 84.37 &
84.37\tabularnewline
dR-8 & dR-8 & 6.13 & 6.13 & -0.64 & -0.64 & 0.56 & 0.56 & 86.71 &
86.71\tabularnewline
dR-103 & dR-103 & 7.29 & 7.29 & 0.60 & 0.60 & 1.16 & 1.16 & 85.73 &
85.73\tabularnewline
dR-113 & dR-113 & 5.38 & 5.38 & -0.01 & -0.01 & 0.67 & 0.67 & 75.49 &
75.49\tabularnewline
dR\textsubscript{I} & dR\textsubscript{I} & 8.57 & 8.57 & -1.25 & -1.25
& 0.68 & 0.68 & 76.82 & 76.82\tabularnewline
dR\textsubscript{I}-4 & dR\textsubscript{I}-4 & 6.31 & 6.31 & -0.65 &
-0.65 & 0.58 & 0.58 & 87.62 & 87.62\tabularnewline
dR\textsubscript{I}-28 & dR\textsubscript{I}-28 & 8.29 & 8.29 & -0.56 &
-0.56 & 0.84 & 0.84 & 86.21 & 86.21\tabularnewline
dR\textsubscript{I}-36 & dR\textsubscript{I}-36 & 8.52 & 8.52 & -0.53 &
-0.53 & 0.88 & 0.88 & 86.17 & 86.17\tabularnewline
dR\textsubscript{I}-44 & dR\textsubscript{I}-44 & 7.43 & 7.43 & 0.41 &
0.41 & 1.09 & 1.09 & 78.67 & 78.67\tabularnewline
dR\textsubscript{I}-49 & dR\textsubscript{I}-49 & 4.87 & 4.87 & -0.12 &
-0.12 & 0.56 & 0.56 & 65.82 & 65.82\tabularnewline
dR\textsubscript{I}-84 & dR\textsubscript{I}-84 & 7.87 & 7.87 & -0.60 &
-0.60 & 0.78 & 0.78 & 77.58 & 77.58\tabularnewline
dR\textsubscript{I}-97 & dR\textsubscript{I}-97 & 6.68 & 6.68 & 0.10 &
0.10 & 0.87 & 0.87 & 86.54 & 86.54\tabularnewline
dR\textsubscript{II} & dR\textsubscript{II} & 8.63 & 8.63 & -1.36 &
-1.36 & 0.66 & 0.66 & 77.04 & 77.04\tabularnewline
dR\textsubscript{II}-2 & dR\textsubscript{II}-2 & 8.18 & 8.18 & -1.33 &
-1.33 & 0.62 & 0.62 & 78.06 & 78.06\tabularnewline
dR\textsubscript{II}-10 & dR\textsubscript{II}-10 & 7.61 & 7.61 & -1.26
& -1.26 & 0.57 & 0.57 & 77.92 & 77.92\tabularnewline
dR\textsubscript{II}-22 & dR\textsubscript{II}-22 & 5.12 & 5.12 & 0.75 &
0.75 & 0.99 & 0.99 & 54.81 & 54.81\tabularnewline
Anionic & Anionic & & & & & & & &\tabularnewline
dR-103 & dR-103 & 4.11 & 4.11 & -2.95 & -2.95 & 0.02 & 0.02 & 77.14 &
77.14\tabularnewline
dR-113 & dR-113 & 2.50 & 2.50 & -3.17 & -3.17 & 0.01 & 0.01 & 75.01 &
75.01\tabularnewline
dR\textsubscript{I}-28 & dR\textsubscript{I}-28 & 2.69 & 2.69 & -2.67 &
-2.67 & 0.000 & 0.000 & 85.09 & 85.09\tabularnewline
dR\textsubscript{I}-36 & dR\textsubscript{I}-36 & 2.62 & 2.62 & -2.57 &
-2.57 & 0.000 & 0.000 & 86.26 & 86.26\tabularnewline
dR\textsubscript{I}-44 & dR\textsubscript{I}-44 & 1.79 & 1.79 & -1.63 &
-1.63 & 0.001 & 0.001 & 78.67 & 78.67\tabularnewline
dR\textsubscript{I}-49 & dR\textsubscript{I}-49 & 1.24 & 1.24 & -2.53 &
-2.53 & 0.06 & 0.06 & 85.74 & 85.74\tabularnewline
dR\textsubscript{II}-22 & dR\textsubscript{II}-22 & 2.06 & 2.06 & -3.51
& -3.51 & 0.05 & 0.05 & 77.61 & 77.61\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}

\textbf{Figure 4} . The electron and hydrogen donating ability map for
antioxidants (eH-DAMA), including the acid-base species of rasagiline
derivatives, the parent molecule, Trolox and the oxidant the
H\textsubscript{2}O\textsubscript{2}/O\textsubscript{2}\textsuperscript{*-}pair.

\section*{Conclusions}

{\label{conclusions}}

A systematic rational search for newly designed rasagiline derivatives
is presented. It was performed using the CADMA-Chem computer-assisted
protocol. A total of 361 derivatives were generated by adding, or
modifying, functional groups in the rasagiline molecular framework.

A selection score (S\textsuperscript{S}) was built to sample the search
space, simultaneously considering ADME (absorption, distribution,
metabolism, excretion) properties, toxicity, and manufacturability
(i.e., synthetic accessibility). It was used to characterize the whole
set of designed derivatives and allowed the selection of a reduced
subset of ten compounds expected to be the most promising, regarding
drug-like behavior.

For this subset, several reactivity indices were estimated, as well as
their pKa values. These indices account for electron and H donor
capabilities. Thus, they are expected to reflect free radical scavenging
behavior through single electron transfer (SET) and formal hydrogen
transfer (HAT) mechanisms. According to the gathered data, three
rasagiline derivatives were identified as the most likely candidates to
act as chemical antioxidants (by directly scavenging free radicals).
They are: dR-113, dR\textsubscript{I}-49 and dR\textsubscript{II}-22 in
that order. All of them are predicted to be better for that purpose than
rasagiline itself. In addition, since the made structural modifications
are mild, they are expected to retain the neuroprotection of the parent
molecule. The findings from this work are expected to motivate further
investigations on these molecules, using both theoretical and
experimental approaches.

\section*{Acknowledgements}

{\label{acknowledgements}}

We gratefully thank the Laboratorio de Visualizaci\selectlanguage{ngerman}ón y Cómputo Paralelo
at Universidad Autónoma Metropolitana-Iztapalapa for computing time.
E.G.G.L. acknowledges CONACyT for Doctoral fellowship.

References

1. Poewe, W.; Seppi, K.; Tanner, C. M.; Halliday, G. M.; Brundin, P.;
Volkmann, J.; Schrag, A.-E.; Lang, A. E., Parkinson disease.\emph{Nature
Reviews Disease Primers} \textbf{2017,} \emph{3} , 17013.

2. Kalia, L. V.; Lang, A. E., Parkinson's disease.
\emph{Lancet}\textbf{2015,} \emph{386} , 896.

3. 2021 alzheimer's disease facts and figures. \emph{Alzheimer's \&
Dementia} \textbf{2021,} \emph{17} , 327-406.

4. Querfurth, H. W.; LaFerla, F. M., Alzheimer's disease. \emph{New
England Journal of Medicine} \textbf{2010,} \emph{362} , 329-344.

5. Kiernan, M. C.; Vucic, S.; Cheah, B. C.; Turner, M. R.; Eisen, A.;
Hardiman, O.; Burrell, J. R.; Zoing, M. C., Amyotrophic lateral
sclerosis. \emph{The Lancet} \textbf{2011,} \emph{377} , 942-955.

6. Davis, A. A.; Leyns, C. E. G.; Holtzman, D. M., Intercellular spread
of protein aggregates in neurodegenerative disease. \emph{Annual Review
of Cell and Developmental Biology} \textbf{2018,} \emph{34} , 545-568.

7. Walker, L. C.; Jucker, M., Neurodegenerative diseases: Expanding the
prion concept. \emph{Annual Review of Neuroscience}
\textbf{2015,}\emph{38} , 87-103.

8. Jucker, M.; Walker, L. C., Pathogenic protein seeding in alzheimer
disease and other neurodegenerative disorders. \emph{Annals of
Neurology} \textbf{2011,} \emph{70} , 532-540.

9. Tang, Z.; Zhao, P.; Wang, H.; Liu, Y.; Bu, W., Biomedicine meets
fenton chemistry. \emph{Chemical Reviews} \textbf{2021} ,
10.1021/acs.chemrev.0c00977.

10. Cicero, C. E.; Mostile, G.; Vasta, R.; Rapisarda, V.; Signorelli, S.
S.; Ferrante, M.; Zappia, M.; Nicoletti, A., Metals and
neurodegenerative diseases. A systematic review. \textbf{2020} .

11. Leal, M. F. C.; Catarino, R. I. L.; Pimenta, A. M.; Souto, M. R. S.,
Roles of metal microelements in neurodegenerative
diseases.\emph{Neurophysiology} \textbf{2020,} \emph{52} , 80-88.

12. Wang, L.; Yin, Y. L.; Liu, X. Z.; Shen, P.; Zheng, Y. G.; Lan, X.
R.; Lu, C. B.; Wang, J. Z., Current understanding of metal ions in the
pathogenesis of alzheimer's disease. \textbf{2020} .

13. Yan, N.; Zhang, J., Iron metabolism, ferroptosis, and the links with
alzheimer's disease. \emph{Frontiers in Neuroscience}
\textbf{2020,}\emph{13} .

14. Martins, A. C.; Gubert, P.; Villas Boas, G. R.; Meirelles Paes, M.;
Santamaría, A.; Lee, E.; Tinkov, A. A.; Bowman, A. B.; Aschner, M.,
Manganese-induced neurodegenerative diseases and possible therapeutic
approaches. \emph{Expert Review of Neurotherapeutics}
\textbf{2020,}\emph{20} , 1109-1121.

15. Ndayisaba, A.; Kaindlstorfer, C.; Wenning, G. K., Iron in
neurodegeneration -- cause or consequence? \emph{Frontiers in
Neuroscience} \textbf{2019,} \emph{13} .

16. Sussulini, A.; Hauser-Davis, R. A., Metallomics applied to the~study
of neurodegenerative and mental diseases. \textbf{2018} .

17. Nam, E.; Han, J.; Suh, J. M.; Yi, Y.; Lim, M. H., Link of impaired
metal ion homeostasis to mitochondrial dysfunction in
neurons.\emph{Curr. Opin. Chem. Biol.} \textbf{2018,} \emph{43} , 8.

18. Zucca, F. A.; Segura-Aguilar, J.; Ferrari, E.; Muñoz, P.; Paris, I.;
Sulzer, D.; Sarna, T.; Casella, L.; Zecca, L., Interactions of iron,
dopamine and neuromelanin pathways in brain aging and parkinson's
disease. \emph{Progress in Neurobiology} \textbf{2017,} \emph{155} ,
96-119.

19. Lanza, V.; D'Agata, R.; Iacono, G.; Bellia, F.; Spoto, G.; Vecchio,
G., Cyclam glycoconjugates as lectin ligands and protective agents of
metal-induced amyloid aggregation. \emph{J. Inorg.
Biochem.}\textbf{2015,} \emph{153} , 377.

20. Barnham, K. J.; Bush, A. I., Biological metals and metal-targeting
compounds in major neurodegenerative diseases. \emph{Chem. Soc.
Rev.}\textbf{2014,} \emph{43} , 6727.

21. Lee, H. J.; Korshavn, K. J.; Kochi, A.; Derrick, J. S.; Lim, M. H.,
Cholesterol and metal ions in alzheimer's disease. \emph{Chem. Soc.
Rev.} \textbf{2014,} \emph{43} , 6672.

22. Faller, P.; Hureau, C.; La Penna, G., Metal ions and intrinsically
disordered proteins and peptides: From cu/zn amyloid-\selectlanguage{greek}β \selectlanguage{english}to general
principles. \emph{Acc. Chem. Res.} \textbf{2014,} \emph{47} , 2252.

23. Bourassa, M. W.; Brown, H. H.; Borchelt, D. R.; Vogt, S.; Miller, L.
M., Metal-deficient aggregates and diminished copper found in cells
expressing sod1mutations that cause als. \emph{Front. Aging
Neurosci.}\textbf{2014,} \emph{6} , 110.

24. Savelieff, M. G.; Lee, S.; Liu, Y.; Lim, M. H., Untangling
amyloid-\selectlanguage{greek}β, \selectlanguage{english}tau, and metals in alzheimer's disease. \emph{ACS Chem.
Biol.} \textbf{2013,} \emph{8} , 856.

25. Greenough, M. A.; Camakaris, J.; Bush, A. I., Metal dyshomeostasis
and oxidative stress in alzheimer's disease. \emph{Neurochem.
Int.}\textbf{2013,} \emph{62} , 540.

26. Faller, P.; Hureau, C.; Berthoumieu, O., Role of metal ions in the
self-assembly of the alzheimer's amyloid-\selectlanguage{greek}β \selectlanguage{english}peptide. \emph{Inorg.
Chem.}\textbf{2013,} \emph{52} , 12193.

27. Kozlowski, H.; Luczkowski, M.; Remelli, M.; Valensin, D., Copper,
zinc and iron in neurodegenerative diseases (alzheimer's, parkinson's
and prion diseases). \emph{Coordination Chemistry Reviews}\textbf{2012,}
\emph{256} , 2129-2141.

28. Viles, J. H., Metal ions and amyloid fiber formation in
neurodegenerative diseases. Copper, zinc and iron in alzheimer's,
parkinson's and prion diseases. \emph{Coordination Chemistry
Reviews}\textbf{2012,} \emph{256} , 2271-2284.

29. T\selectlanguage{ngerman}õugu, V.; Tiiman, A.; Palumaa, P., Interactions of zn(ii) and
cu(ii) ions with alzheimer's amyloid-beta peptide. Metal ion binding,
contribution to fibrillization and toxicity.
\emph{Metallomics}\textbf{2011,} \emph{3} , 250.

30. Lelie, H. L.; Liba, A.; Bourassa, M. W.; Chattopadhyay, M.; Chan, P.
K.; Gralla, E. B.; Miller, L. M.; Borchelt, D. R.; Valentine, J. S.;
Whitelegge, J. P., Copper and zinc metallation status of copper-zinc
superoxide dismutase from amyotrophic lateral sclerosis transgenic
mice.\emph{J. Biol. Chem.} \textbf{2011,} \emph{286} , 2795.

31. Jomova, K.; Vondrakova, D.; Lawson, M.; Valko, M., Metals, oxidative
stress and neurodegenerative disorders. \emph{Mol. Cell.
Biochem.}\textbf{2010,} \emph{345} , 91.

32. Rivera-Mancía, S.; Pérez-Neri, I.; Ríos, C.; Tristán-López, L.;
Rivera-Espinosa, L.; Montes, S., The transition metals copper and iron
in neurodegenerative diseases. \emph{Chemico-Biological
Interactions}\textbf{2010,} \emph{186} , 184-199.

33. Balboni, E.; Filippini, T.; Crous-Bou, M.; Guxens, M.; Erickson, L.
D.; Vinceti, M., The association between air pollutants and hippocampal
volume from magnetic resonance imaging: A systematic review and
meta-analysis. \emph{Environmental Research} \textbf{2022,} \emph{204} ,
111976.

34. Tham, R.; Schikowski, T., The role of traffic-related air pollution
on neurodegenerative diseases in older people: An epidemiological
perspective. \emph{Journal of Alzheimer's Disease}
\textbf{2021,}\emph{79} , 949-959.

35. Nunez, Y.; Boehme, A. K.; Li, M.; Goldsmith, J.; Weisskopf, M. G.;
Re, D. B.; Navas-Acien, A.; van Donkelaar, A.; Martin, R. V.;
Kioumourtzoglou, M.-A., Parkinson's disease aggravation in association
with fine particle components in new york state. \emph{Environmental
Research} \textbf{2021,} \emph{201} , 111554.

36. Costa, L. G.; Cole, T. B.; Dao, K.; Chang, Y.-C.; Coburn, J.;
Garrick, J. M., Effects of air pollution on the nervous system and its
possible role in neurodevelopmental and neurodegenerative
disorders.\emph{Pharmacology \& Therapeutics} \textbf{2020,} \emph{210}
, 107523.

37. Cory\selectlanguage{english}-slechta, D. A.; Sobolewski, M.; Oberdorster, G., Air
pollution\selectlanguage{english}-related brain metal dyshomeostasis as a potential risk factor
for neurodevelopmental disorders and neurodegenerative
diseases.\emph{Atmosphere} \textbf{2020,} \emph{11} .

38. Kasdagli, M.-I.; Katsouyanni, K.; Dimakopoulou, K.; Samoli, E., Air
pollution and parkinson's disease: A systematic review and meta-analysis
up to 2018. \emph{International Journal of Hygiene and Environmental
Health} \textbf{2019,} \emph{222} , 402-409.

39. Bihaqi, S. W., Early life exposure to lead (pb) and changes in DNA
methylation: Relevance to alzheimer's disease. \emph{Reviews on
Environmental Health} \textbf{2019,} \emph{34} , 187-195.

40. Croze, M. L.; Zimmer, L., Ozone atmospheric pollution and
alzheimer's disease: From epidemiological facts to molecular
mechanisms.\emph{Journal of Alzheimer's Disease} \textbf{2018,}
\emph{62} , 503-522.

41. Li, X.; Yu, J.; Li, J.; Wu, Y.; Li, B., Dopaminergic dysfunction in
mammalian dopamine neurons induced by simazine
neurotoxicity.\emph{International Journal of Molecular Sciences}
\textbf{2017,}\emph{18} .

42. Su, F.-C.; Goutman, S. A.; Chernyak, S.; Mukherjee, B.; Callaghan,
B. C.; Batterman, S.; Feldman, E. L., Association of environmental
toxins with amyotrophic lateral sclerosis. \emph{JAMA
Neurology}\textbf{2016,} \emph{73} , 803-811.

43. Mushtaq, G.; A. Khan, J.; Joseph, E.; A. Kamal, M., Nanoparticles,
neurotoxicity and neurodegenerative diseases. \emph{Current Drug
Metabolism} \textbf{2015,} \emph{16} , 676-684.

44. Chin-Chan, M.; Navarro-Yepes, J.; Quintanilla-Vega, B.,
Environmental pollutants as risk factors for neurodegenerative
disorders: Alzheimer and parkinson diseases. \emph{Front Cell
Neurosci}\textbf{2015,} \emph{9} , 124-124.

45. Aung, K. H.; Tsukahara, S.; Maekawa, F.; Nohara, K.; Nakamura, K.;
Tanoue, A., Role of environmental chemical insult in neuronal cell death
and cytoskeleton damage. \emph{Biological and Pharmaceutical
Bulletin}\textbf{2015,} \emph{38} , 1109-1112.

46. Landrigan, P. J.; Sonawane, B.; Butler, R. N.; Trasande, L.; Callan,
R.; Droller, D., Early environmental origins of neurodegenerative
disease in later life. \emph{Environ Health Perspect}
\textbf{2005,}\emph{113} , 1230-1233.

47. Brown Rebecca, C.; Lockwood Alan, H.; Sonawane Babasaheb, R.,
Neurodegenerative diseases: An overview of environmental risk
factors.\emph{Environ Health Perspect} \textbf{2005,} \emph{113} ,
1250-1256.

48. Nishimura, Y.; Kanda, Y.; Sone, H.; Aoyama, H., Oxidative stress as
a common key event in developmental neurotoxicity. \emph{OXIDATIVE
MEDICINE AND CELLULAR LONGEVITY} \textbf{2021,} \emph{2021} .

49. Dorszewska, J.; Kowalska, M.; Prendecki, M.; Piekut, T.; Kozlowska,
J.; Kozubski, W., Oxidative stress factors in parkinson's
disease.\emph{NEURAL REGENERATION RESEARCH} \textbf{2021,} \emph{16} ,
1383-1391.

50. Saleem, U.; Sabir, S.; Niazi, S. G.; Naeem, M.; Ahmad, B., Role of
oxidative stress and antioxidant defense biomarkers in neurodegenerative
diseases. \emph{CRITICAL REVIEWS IN EUKARYOTIC GENE
EXPRESSION}\textbf{2020,} \emph{30} , 311-322.

51. Singh, A.; Kukreti, R.; Saso, L.; Kukreti, S., Oxidative stress: A
key modulator in neurodegenerative diseases.
\emph{MOLECULES}\textbf{2019,} \emph{24} .

52. Butterfield, D. A.; Halliwell, B., Oxidative stress, dysfunctional
glucose metabolism and alzheimer disease. \emph{Nat. Rev.
Neurosci.}\textbf{2019,} \emph{20} , 148.

53. Cheignon, C.; Tomas, M.; Bonnefont-Rousselot, D.; Faller, P.;
Hureau, C.; Collin, F., Oxidative stress and the amyloid beta peptide in
alzheimer's disease. \emph{Redox Biol.} \textbf{2018,} \emph{14} , 450.

54. Butterfield, D. A., Perspectives on oxidative stress in alzheimer's
disease and predictions of future research emphases. \emph{JOURNAL OF
ALZHEIMERS DISEASE} \textbf{2018,} \emph{64} , S469-S479.

55. Ganguly, G.; Chakrabarti, S.; Chatterjee, U.; Saso, L.,
Proteinopathy, oxidative stress and mitochondrial dysfunction: Cross
talk in alzheimer's disease and parkinson's disease. \emph{Drug Des.,
Dev. Ther.} \textbf{2017,} \emph{11} , 797.

56. Jiang, T. F.; Sun, Q.; Chen, S. D., Oxidative stress: A major
pathogenesis and potential therapeutic target of antioxidative agents in
parkinson's disease and alzheimer's disease. \emph{PROGRESS IN
NEUROBIOLOGY} \textbf{2016,} \emph{147} , 1-19.

57. Kim, G. H.; Kim, J. E.; Rhie, S. J.; Yoon, S., The role of oxidative
stress in neurodegenerative diseases. \emph{EXPERIMENTAL
NEUROBIOLOGY}\textbf{2015,} \emph{24} , 325-340.

58. Ferreira, M. E. S.; de Vasconcelos, A. S.; Vilhena, T. D.; da Silva,
T. L.; Barbosa, A. D.; Gomes, A. R. Q.; Dolabela, M. F.; Percario, S.,
Oxidative stress in alzheimer's disease: Should we keep trying
antioxidant therapies? \emph{CELLULAR AND MOLECULAR
NEUROBIOLOGY}\textbf{2015,} \emph{35} , 595-614.

59. Blesa, J.; Trigo-Damas, I.; Quiroga-Varela, A.; Jackson-Lewis, V.
R., Oxidative stress and parkinson's disease. \emph{Front.
Neuroanat.}\textbf{2015,} \emph{9} , 91.

60. Radi, E.; Formichi, P.; Battisti, C.; Federico, A., Apoptosis and
oxidative stress in neurodegenerative diseases. \emph{J. Alzheimer's
Dis.} \textbf{2014,} \emph{42} , S125.

61. Chen, Z.; Zhong, C., Oxidative stress in alzheimer's
disease.\emph{Neurosci. Bull.} \textbf{2014,} \emph{30} , 271.

62. Zhao, Y.; Zhao, B. L., Oxidative stress and the pathogenesis of
alzheimer's disease. \emph{OXIDATIVE MEDICINE AND CELLULAR
LONGEVITY}\textbf{2013,} \emph{2013} .

63. Subramaniam, S. R.; Chesselet, M. F., Mitochondrial dysfunction and
oxidative stress in parkinson's disease. \emph{Prog.
Neurobiol.}\textbf{2013,} \emph{106--107} , 17.

64. Guo, C. Y.; Sun, L.; Chen, X. P.; Zhang, D. S., Oxidative stress,
mitochondrial damage and neurodegenerative diseases. \emph{NEURAL
REGENERATION RESEARCH} \textbf{2013,} \emph{8} , 2003-2014.

65. Barnham, K. J.; Masters, C. L.; Bush, A. I., Neurodegenerative
diseases and oxidative stress. \emph{Nat. Rev. Drug
Discovery}\textbf{2004,} \emph{3} , 205.

66. Radi, R., The oxygen paradox in human biology and medicine:
Bioenergetics, free radicals and oxidative stress. \emph{FEBS OPEN
BIO}\textbf{2021,} \emph{11} , 7-7.

67. Guo, Q. P.; Li, F. N.; Duan, Y. H.; Wen, C. Y.; Wang, W. L.; Zhang,
L. Y.; Huang, R. L.; Yin, Y. L., Oxidative stress, nutritional
antioxidants and beyond. \emph{SCIENCE CHINA-LIFE
SCIENCES}\textbf{2020,} \emph{63} , 866-874.

68. Matschke, V.; Theiss, C.; Matschke, J., Oxidative stress: The lowest
common denominator of multiple diseases. \emph{NEURAL REGENERATION
RESEARCH} \textbf{2019,} \emph{14} , 238-241.

69. Bose, A.; Beal, M. F., Mitochondrial dysfunction and oxidative
stress in induced pluripotent stem cell models of parkinson's
disease.\emph{Eur. J. Neurosci.} \textbf{2019,} \emph{49} , 525.

70. Castaneda-Arriaga, R.; Perez-Gonzalez, A.; Reina, M.;
Alvarez-Idaboy, J. R.; Galano, A., Comprehensive investigation of the
antioxidant and pro-oxidant effects of phenolic compounds: A
double-edged sword in the context of oxidative stress? \emph{The Journal
of Physical Chemistry B} \textbf{2018,} \emph{122} , 6198-6214.

71. Sies, H.; Berndt, C.; Jones, D. P., Oxidative stress. \emph{Annual
Review of Biochemistry} \textbf{2017,} \emph{86} , 715-748.

72. Ortiz, G. G.; Moises, F. P. P.; Mireles-Ramirez, M.;
Flores-Alvarado, L. J.; Gonzalez-Usigli, H.; Sanchez-Gonzalez, V. J.;
Sanchez-Lopez, A. L.; Sanchez-Romero, L.; Diaz-Barba, E. I.;
Santoscoy-Gutierrez, J. F., et al., Oxidative stress: Love and hate
history in central nervous system. In \emph{Stress and inflammation in
disorders} , Donev, R., Ed. 2017; Vol. 108, pp 1-31.

73. Sies, H., Oxidative stress: A concept in redox biology and
medicine.\emph{Redox Biol} \textbf{2015,} \emph{4} , 180-183.

74. Xu, J. Z.; Leeuwenburgh, C., Free radicals and oxidative stress:
Basic concepts and misconceptions. In \emph{Free radicals in ent
pathology} , Miller, J.; LePrell, C. G.; Rybak, L., Eds. 2015,
10.1007/978-3-319-13473-4\_2

10.1007/978-3-319-13473-4pp 9-20.

75. Lushchak, V. I., Free radicals, reactive oxygen species, oxidative
stress and its classification. \emph{CHEMICO-BIOLOGICAL
INTERACTIONS}\textbf{2014,} \emph{224} , 164-175.

76. Kaur, R.; Kaur, J.; Mahajan, J.; Kumar, R.; Arora, S., Oxidative
stress-implications, source and its prevention. \emph{ENVIRONMENTAL
SCIENCE AND POLLUTION RESEARCH} \textbf{2014,} \emph{21} , 1599-1613.

77. Pradeep, H.; Diya, J. B.; Shashikumar, S.; Rajanikant, G. K.,
Oxidative stress - assassin behind the ischemic stroke. \emph{FOLIA
NEUROPATHOLOGICA} \textbf{2012,} \emph{50} , 219-230.

78. Mandelker, L., Oxidative stress, free radicals, and cellular damage.
In \emph{Studies on veterinary medicine} , Mandelker, L.; Vajdovich, P.,
Eds. 2011, 10.1007/978-1-61779-071-3\_1

10.1007/978-1-61779-071-3pp 1-17.

79. Poprac, P.; Jomova, K.; Simunkova, M.; Kollar, V.; Rhodes, C. J.;
Valko, M., Targeting free radicals in oxidative stress-related human
diseases. \emph{TRENDS IN PHARMACOLOGICAL SCIENCES}
\textbf{2017,}\emph{38} , 592-607.

80. Gebicki, J. M., Oxidative stress, free radicals and protein
peroxides. \emph{ARCHIVES OF BIOCHEMISTRY AND BIOPHYSICS}
\textbf{2016,}\emph{595} , 33-39.

81. Galano, A., Free radicals induced oxidative stress at a molecular
level: The current status, challenges and perspectives of computational
chemistry based protocols. \emph{JOURNAL OF THE MEXICAN CHEMICAL
SOCIETY} \textbf{2015,} \emph{59} , 231-262.

82. Richardson, A., Is the free radical (oxidative stress) theory of
aging dead? \emph{GERONTOLOGIST} \textbf{2012,} \emph{52} , 405-405.

83. Gul, A.; Rahman, M. A.; Hamid, S., Biochemistry of antioxidants and
their role in oxidative stress caused by free radicals. \emph{OXIDATION
COMMUNICATIONS} \textbf{2010,} \emph{33} , 682-697.

84. Jensen, S. J. K., Oxidative stress and free radicals. \emph{Journal
of Molecular Structure: THEOCHEM} \textbf{2003,} \emph{666-667} ,
387-392.

85. Collin, F., Chemical basis of reactive oxygen species reactivity and
involvement in neurodegenerative diseases. \emph{International Journal
of Molecular Sciences} \textbf{2019,} \emph{20} .

86. Friedman, J., Why is the nervous system vulnerable to oxidative
stress? In \emph{Oxidative stress and free radical damage in neurology}
, Gadoth, N.; Gobel, H. H., Eds. 2011, 10.1007/978-1-60327-514-9\_2

10.1007/978-1-60327-514-9pp 19-27.

87. Schoneich, C., Reactive oxygen species and biological aging: A
mechanistic approach. \emph{Experimental Gerontology}
\textbf{1999,}\emph{34} , 19-34.

88. Plotnikov, M. B.; Plotnikova, T. M., Tyrosol as a neuroprotector:
Strong effects of a ''weak'' antioxidant. \emph{CURRENT
NEUROPHARMACOLOGY}\textbf{2021,} \emph{19} , 434-448.

89. Singh, E.; Devasahayam, G., Neurodegeneration by oxidative stress: A
review on prospective use of small molecules for
neuroprotection.\emph{MOLECULAR BIOLOGY REPORTS} \textbf{2020,}
\emph{47} , 3133-3140.

90. Lee, K. H.; Cha, M.; Lee, B. H., Neuroprotective effect of
antioxidants in the brain. \emph{International Journal of Molecular
Sciences} \textbf{2020,} \emph{21} .

91. Teleanu, R. I.; Chircov, C.; Grumezescu, A. M.; Volceanov, A.;
Teleanu, D. M., Antioxidant therapies for neuroprotection-a
review.\emph{JOURNAL OF CLINICAL MEDICINE} \textbf{2019,} \emph{8} .

92. Lloret, A.; Esteve, D.; Monllor, P.; Cervera-Ferri, A.; Lloret, A.,
The effectiveness of vitamin e treatment in alzheimer's
disease.\emph{International Journal of Molecular Sciences}
\textbf{2019,}\emph{20} .

93. Watson, N.; Diamandis, T.; Gonzales-Portillo, C.; Reyes, S.;
Borlongan, C. V., Melatonin as an antioxidant for stroke
neuroprotection. \emph{CELL TRANSPLANTATION} \textbf{2016,} \emph{25} ,
883-891.

94. Lalkovicova, M.; Danielisova, V., Neuroprotection and
antioxidants.\emph{NEURAL REGENERATION RESEARCH} \textbf{2016,}
\emph{11} , 865-874.

95. Dohi, K.; Satoh, K.; Nakamachi, T.; Yofu, S.; Hiratsuka, K.;
Nakamura, S.; Ohtaki, H.; Yoshikawa, T.; Shioda, S.; Aruga, T., Does
edaravone (mci-186) act as an antioxidant and a neuroprotector in
experimental traumatic brain injury? \emph{ANTIOXIDANTS \& REDOX
SIGNALING} \textbf{2007,} \emph{9} , 281-287.

96. Mohanakumar, K. P.; Thomas, B.; Sharma, S. M.; Muralikrishnan, D.;
Chowdhury, R.; Chiueh, C. C., Nitric oxide - an antioxidant and
neuroprotector. In \emph{Nitric oxide: Novel actions, deleterious
effects and clinical potential} , Chiueh, C. C.; Hong, J. S.; Leong, S.
K., Eds. 2002; Vol. 962, pp 389-401.

97. Behl, C.; Moosmann, B., Antioxidant neuroprotection in alzheimer's
disease as preventive and therapeutic approach2 2this article is part of
a series of reviews on ``causes and consequences of oxidative stress in
alzheimer's disease.'' the full list of papers may be found on the
homepage of the journal. \emph{Free Radical Biology and
Medicine}\textbf{2002,} \emph{33} , 182-191.

98. Finberg, J. P. M., The discovery and development of rasagiline as a
new anti-parkinson medication. \emph{JOURNAL OF NEURAL
TRANSMISSION}\textbf{2020,} \emph{127} , 125-130.

99. Stocchi, F.; Fossati, C.; Torti, M., Rasagiline for the treatment of
parkinson's disease: An update. \emph{EXPERT OPINION ON
PHARMACOTHERAPY}\textbf{2015,} \emph{16} , 2231-2241.

100. Muller, T., Pharmacokinetic/pharmacodynamic evaluation of
rasagiline mesylate for parkinson's disease. \emph{EXPERT OPINION ON
DRUG METABOLISM \& TOXICOLOGY} \textbf{2014,} \emph{10} , 1423-1432.

101. McCormack, P. L., Rasagiline: A review of its use in the treatment
of idiopathic parkinson's disease. \emph{CNS DRUGS}
\textbf{2014,}\emph{28} , 1083-1097.

102. Minguez-Minguez, S.; del Pozo, J. S. G.; Jordan, J., Rasagiline in
parkinson's disease: A review based on meta-analysis of clinical
data.\emph{PHARMACOLOGICAL RESEARCH} \textbf{2013,} \emph{74} , 78-86.

103. Hoy, S. M.; Keating, G. M., Rasagiline a review of its use in the
treatment of idiopathic parkinson's disease. \emph{DRUGS}
\textbf{2012,}\emph{72} , 643-669.

104. Chen, J. J.; Wilkinson, J. R., The monoamine oxidase type b
inhibitor rasagiline in the treatment of parkinson disease: Is tyramine
a challenge? \emph{JOURNAL OF CLINICAL PHARMACOLOGY}
\textbf{2012,}\emph{52} , 620-628.

105. Perez-Lloret, S.; Rascol, O., Safety of rasagiline for the
treatment of parkinson's disease. \emph{EXPERT OPINION ON DRUG
SAFETY}\textbf{2011,} \emph{10} , 633-643.

106. Chahine, L. M.; Stern, M. B., Rasagiline in parkinson's disease.
In\emph{Monoamine oxidases and their inhibitors} , Youdim, M. B. H.;
Riederer, P., Eds. 2011; Vol. 100, pp 151-168.

107. Pagonabarraga, J.; Kulisevsky, J., Rasagiline: Effectiveness and
protection in parkinson's disease. \emph{REVISTA DE
NEUROLOGIA}\textbf{2010,} \emph{51} , 535-541.

108. Leegwater-Kim, J.; Bortan, E., The role of rasagiline in the
treatment of parkinson's disease. \emph{CLINICAL INTERVENTIONS IN
AGING}\textbf{2010,} \emph{5} , 149-156.

109. Linazasoro, G., Rasagiline in parkinson's disease.\emph{NEUROLOGIA}
\textbf{2008,} \emph{23} , 238-245.

110. Oldfield, V.; Keating, G. M.; Perry, C. M., Rasagiline - a review
of its use in the management of parkinson's disease.
\emph{DRUGS}\textbf{2007,} \emph{67} , 1725-1747.

111. Chen, J. J.; Swope, D. M.; Dashtipour, K., Comprehensive review of
rasagiline, a second-generation monoamine oxidase inhibitor, for the
treatment of parkinson's disease. \emph{CLINICAL
THERAPEUTICS}\textbf{2007,} \emph{29} , 1825-1849.

112. Chen, J. J.; Ly, A. V., Rasagiline: A second-generation monoamine
oxidase type-b inhibitor for the treatment of parkinson's
disease.\emph{AMERICAN JOURNAL OF HEALTH-SYSTEM PHARMACY}
\textbf{2006,}\emph{63} , 915-928.

113. Rascol, O., Rasagiline in the pharmacotherapy of parkinson's
disease - a review. \emph{EXPERT OPINION ON
PHARMACOTHERAPY}\textbf{2005,} \emph{6} , 2061-2075.

114. Blandini, F., Neuroprotection by rasagiline: A new therapeutic
approach to parkinson's disease? \emph{CNS DRUG REVIEWS}
\textbf{2005,}\emph{11} , 183-194.

115. Jiang, D. Q.; Wang, H. K.; Wang, Y.; Li, M. X.; Jiang, L. L.; Wang,
Y., Rasagiline combined with levodopa therapy versus levodopa
monotherapy for patients with parkinson's disease: A systematic
review.\emph{NEUROLOGICAL SCIENCES} \textbf{2020,} \emph{41} , 101-109.

116. Pagonabarraga, J.; Rodriguez-Oroz, M. C., Rasagiline in monotherapy
in patients with early stages of parkinson's disease and in combined and
adjunct therapy to levodopa with moderate and advanced
stages.\emph{REVISTA DE NEUROLOGIA} \textbf{2013,} \emph{56} , 25-34.

117. Rabey, J. M.; Sagi, I.; Huberman, M.; Melamed, E.; Korczyn, A.;
Giladi, N.; Inzelberg, R.; Djaldetti, R.; Klein, C.; Berecz, G., et al.,
Rasagiline mesylate, a new mao-b inhibitor for the treatment of
parkinson's disease: A double-blind study as adjunctive therapy to
levodopa. \emph{CLINICAL NEUROPHARMACOLOGY} \textbf{2000,} \emph{23} ,
324-330.

118. Youdim, M. B. H.; Kupershmidt, L.; Amit, T.; Weinreb, O., Promises
of novel multi-target neuroprotective and neurorestorative drugs for
parkinson's disease. \emph{Parkinsonism Relat. Disord.}
\textbf{2014,}\emph{20} , S132.

119. Mandel, S.; Weinreb, O.; Amit, T.; Youdim, M. B. H., Mechanism of
neuroprotective action of the anti-parkinson drug rasagiline and its
derivatives. \emph{Brain Research Reviews} \textbf{2005,} \emph{48} ,
379-387.

120. Youdim, M. B. H.; Maruyama, W.; Naoi, M., Neuropharmacolomcal,
neuroprotective and amyloid precursor processing properties of selective
mao-b inhibitor antiparkinsonian drug, rasagiline. \emph{DRUGS OF
TODAY}\textbf{2005,} \emph{41} , 369-391.

121. Weinreb, O.; Amit, T.; Bar-Am, O.; Youdim, M. B. H., Rasagiline: A
novel anti-parkinsonian monoamine oxidase-b inhibitor with
neuroprotective activity. \emph{Prog. Neurobiol.}
\textbf{2010,}\emph{92} , 330.

122. Weinreb, O.; Bar-Am, O.; Prosolovich, K.; Amit, T.; Youdim, M. B.
H., Does 1-(r)-aminoindan possess neuroprotective properties against
experimental parkinson's disease? \emph{Antioxidants and Redox
Signaling} \textbf{2011,} \emph{14} , 767-775.

123. Bar-Am, O.; Weinreb, O.; Amit, T.; Youdim, M. B. H., The
neuroprotective mechanism of 1-(r)-aminoindan, the major metabolite of
the anti-parkinsonian drug rasagiline. \emph{Journal of
Neurochemistry}\textbf{2010,} \emph{112} , 1131-1137.

124. Weinreb, O.; Amit, T.; Bar-Am, O.; Youdim, M. B. H., Ladostigil: A
novel multimodal neuroprotective drug with cholinesterase and
brain-selective monoamine oxidase inhibitory activities for alzheimer's
disease treatment. \emph{Current Drug Targets} \textbf{2012,} \emph{13}
, 483-494.

125. Bar-Am, O.; Amit, T.; Youdim, M. B. H., Aminoindan and
hydroxyaminoindan, metabolites of rasagiline and ladostigil,
respectively, exert neuroprotective properties in vitro. \emph{Journal
of Neurochemistry} \textbf{2007,} \emph{103} , 500-508.

126. Molinspiration cheminformatics free web services.
https://www.molinspiration.com/ (accessed 2020).

127. Drug likeness tool (drulito 1).
http://www.niper.gov.in/pi\_dev\_tools/DruLiToWeb/DruLiTo\_index.html
(accessed February 8, 2020).

128. Ghose, A. K.; Viswanadhan, V. N.; Wendoloski, J. J., A
knowledge-based approach in designing combinatorial or medicinal
chemistry libraries for drug discovery. 1. A qualitative and
quantitative characterization of known drug databases. \emph{J. Comb.
Chem.} \textbf{1999,} \emph{1} , 55.

129. Veber, D. F.; Johnson, S. R.; Cheng, H. Y.; Smith, B. R.; Ward, K.
W.; Kopple, K. D., Molecular properties that influence the oral
bioavailability of drug candidates. \emph{J. Med. Chem.}
\textbf{2002,}\emph{45} , 2615.

130. Lipinski, C. A., Lead- and drug-like compounds: The rule-of-five
revolution. \emph{Drug Discovery Today: Technologies}
\textbf{2004,}\emph{1} , 337-341.

131. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.,
Experimental and computational approaches to estimate solubility and
permeability in drug discovery and development settings. \emph{Advanced
Drug Delivery Reviews} \textbf{2012,} \emph{64} , 4-17.

132. Coley, C. W.; Rogers, L.; Green, W. H.; Jensen, K. F., Scscore:
Synthetic complexity learned from a reaction corpus. \emph{Journal of
Chemical Information and Modeling} \textbf{2018,} \emph{58} , 252-261.

133. Mendez-Lucio, O.; Medina-Franco, J. L., The many roles of molecular
complexity in drug discovery. \emph{Drug Discovery Today}
\textbf{2017,}\emph{22} , 120-126.

134. Fukunishi, Y.; Kurosawa, T.; Mikami, Y.; Nakamura, H., Prediction
of synthetic accessibility based on commercially available compound
databases. \emph{Journal of Chemical Information and
Modeling}\textbf{2014,} \emph{54} , 3259-3267.

135. Boda, K.; Seidel, T.; Gasteiger, J., Structure and reaction based
evaluation of synthetic accessibility. \emph{J. Comput.-Aided Mol.
Des.}\textbf{2007,} \emph{21} , 311.

136. Sylvia. https://mn-am.com/products/sylvia/ (accessed February 8,
2020).

137. Bonnet, P., Is chemical synthetic accessibility computationally
predictable for drug and lead-like molecules? A comparative assessment
between medicinal and computational chemists. \emph{Eur. J. Med.
Chem.}\textbf{2012,} \emph{54} , 679.

138. Bakhtyari, N. G.; Raitano, G.; Benfenati, E.; Martin, T.; Young,
D., Comparison of in silico models for prediction of
mutagenicity.\emph{J Environ Sci Health C Environ Carcinog Ecotoxicol
Rev}\textbf{2013,} \emph{31} , 45-66.

139. Myatt, G. J.; Ahlberg, E.; Akahori, Y.; Allen, D.; Amberg, A.;
Anger, L. T.; Aptula, A.; Auerbach, S.; Beilke, L.; Bellion, P., et al.,
In silico toxicology protocols. \emph{Regulatory Toxicology and
Pharmacology} \textbf{2018,} \emph{96} , 1-17.

140. Castro-Gonzalez, L. M.; Alvarez-Idaboy, J. R.; Galano, A.,
Computationally designed sesamol derivatives proposed as potent
antioxidants. \emph{ACS Omega} \textbf{2020,} \emph{5} , 9566-9575.

141. Castaneda-Arriaga, R.; Perez-Gonzalez, A.; Reina, M.; Galano, A.,
Computer-designed melatonin derivatives: Potent peroxyl radical
scavengers with no pro-oxidant behavior. \emph{Theoretical Chemistry
Accounts} \textbf{2020,} \emph{139} , 133.

142. Reina, M.; Castaneda-Arriaga, R.; Perez-Gonzalez, A.; Guzman-Lopez,
E. G.; Tan, D.-X.; Reiter, R. J.; Galano, A., A computer-assisted
systematic search for melatonin derivatives with high potential as
antioxidants. \emph{Melatonin Research} \textbf{2018,} \emph{1} , 27-58.

143. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.;
Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G.
A.; Nakatsuji, H., et al. \emph{Gaussian 16 rev. C.01} , Wallingford,
CT, 2016.

144. Marenich, A. V.; Cramer, C. J.; Truhlar, D. G., Universal solvation
model based on solute electron density and on a continuum model of the
solvent defined by the bulk dielectric constant and atomic surface
tensions. \emph{J. Phys. Chem. B} \textbf{2009,} \emph{113} , 6378.

145. Zhao, Y.; Schultz, N. E.; Truhlar, D. G., Design of density
functionals by combining the method of constraint satisfaction with
parametrization for thermochemistry, thermochemical kinetics, and
noncovalent interactions. \emph{J. Chem. Theory Comput.}
\textbf{2006,}\emph{2} , 364.

146. Wu, W. h.; Lei, P.; Liu, Q.; Hu, J.; Gunn, A. P.; Chen, M. s.; Rui,
Y. f.; Su, X. y.; Xie, Z. p.; Zhao, Y. F., Sequestration of copper from
\selectlanguage{greek}β-\selectlanguage{english}amyloid promotes selective lysis by cyclen-hybrid cleavage
agents.\emph{J. Biol. Chem.} \textbf{2008,} \emph{283} , 31657.

147. Milenkovi\selectlanguage{polish}ć, \selectlanguage{english}D.; Dorovi\selectlanguage{polish}ć, \selectlanguage{english}J.; Jeremi\selectlanguage{polish}ć, \selectlanguage{english}S.; Dimitri\selectlanguage{polish}ć \selectlanguage{english}Markovi\selectlanguage{polish}ć, \selectlanguage{english}J. M.;
Avdovi\selectlanguage{polish}ć, \selectlanguage{english}E. H.; Markovi\selectlanguage{polish}ć, \selectlanguage{english}Z., Free radical scavenging potency of
dihydroxybenzoic acids. \emph{Journal of Chemistry}
\textbf{2017,}\emph{2017} .

148. Ami\selectlanguage{polish}ć, \selectlanguage{english}A.; Markovi\selectlanguage{polish}ć, \selectlanguage{english}Z.; Dimitri\selectlanguage{polish}ć \selectlanguage{english}Markovi\selectlanguage{polish}ć, \selectlanguage{english}J. M.; Lu\selectlanguage{polish}č\selectlanguage{english}i\selectlanguage{polish}ć, \selectlanguage{english}B.;
Stepani\selectlanguage{polish}ć, \selectlanguage{english}V.; Ami\selectlanguage{polish}ć, \selectlanguage{english}D., The 2h+/2e- free radical scavenging mechanisms
of uric acid: Thermodynamics of n-h bond cleavage. \emph{Computational
and Theoretical Chemistry} \textbf{2016,} \emph{1077} , 2-10.

149. Dorovi\selectlanguage{polish}ć, \selectlanguage{english}J.; Markovi\selectlanguage{polish}ć, \selectlanguage{english}J. M. D.; Stepani\selectlanguage{polish}ć, \selectlanguage{english}V.; Begovi\selectlanguage{polish}ć, \selectlanguage{english}N.; Ami\selectlanguage{polish}ć,
\selectlanguage{english}D.; Markovi\selectlanguage{polish}ć, \selectlanguage{english}Z., Influence of different free radicals on scavenging
potency of gallic acid. \emph{Journal of Molecular
Modeling}\textbf{2014,} \emph{20} .

150. Markovi\selectlanguage{polish}ć, \selectlanguage{english}Z.; Crossed D Signorovi\selectlanguage{polish}ć, \selectlanguage{english}J.; Deki\selectlanguage{polish}ć, \selectlanguage{english}M.; Radulovi\selectlanguage{polish}ć, \selectlanguage{english}M.;
Markovi\selectlanguage{polish}ć, \selectlanguage{english}S.; Ili\selectlanguage{polish}ć, \selectlanguage{english}M., Dft study of free radical scavenging activity of
erodiol. \emph{Chemical Papers} \textbf{2013,} \emph{67} , 1453-1461.

151. Galano, A.; Alvarez-Idaboy, J. R.; Francisco-M\selectlanguage{ngerman}árquez, M.,
Physicochemical insights on the free radical scavenging activity of
sesamol: Importance of the acid/base equilibrium. \emph{J. Phys. Chem.
B} \textbf{2011,} \emph{115} , 13101.

152. Ortiz, J. V., Toward an exact one-electron picture of chemical
bonding. In \emph{Advances in Quantum Chemistry} , Academic Press Inc.:
1999; Vol. 35, pp 33-52.

153. Ortiz, J. V., Electron propagator theory: An approach to prediction
and interpretation in quantum chemistry. \emph{Wiley Interdiscip. Rev.:
Comput. Mol. Sci.} \textbf{2013,} \emph{3} , 123.

154. Ortiz, J. V., Partial third\selectlanguage{english}-order quasiparticle theory: Comparisons
for closed\selectlanguage{english}-shell ionization energies and an application to the borazine
photoelectron spectrum. \emph{The Journal of Chemical
Physics}\textbf{1996,} \emph{104} , 7599-7605.

155. Perez-Gonzalez, A.; Galano, A.; Ortiz, J. V., Vertical ionization
energies of free radicals and electron detachment energies of their
anions: A comparison of direct and indirect methods versus
experiment.\emph{J. Phys. Chem. A} \textbf{2014,} \emph{118} , 6125.

156. Ortiz, J. V., The electron propagator picture of molecular
electronic structure. In \emph{Computational chemistry: Reviews of
current trends} , WORLD SCIENTIFIC: 1997; Vol. Volume 2, pp 1-61.

157. Chattaraj, P. K.; Roy, D. R., Update 1 of: Electrophilicity
index.\emph{Chemical Reviews} \textbf{2007,} \emph{107} , PR46-PR74.

158. Chattaraj, P. K.; Sarkar, U.; Roy, D. R., Electrophilicity
index.\emph{Chemical Reviews} \textbf{2006,} \emph{106} , 2065-2091.

159. Parr, R. G.; Szentpaly, L. V.; Liu, S., Electrophilicity
index.\emph{Journal of the American Chemical Society}
\textbf{1999,}\emph{121} , 1922-1924.

160. Perez-Gonzalez, A.; Castaneda-Arriaga, R.; Verastegui, B.;
Carreon-Gonzalez, M.; Alvarez-Idaboy, J. R.; Galano, A., Estimation of
empirically fitted parameters for calculating pk a values of thiols in a
fast and reliable way. \emph{Theor. Chem. Acc.} \textbf{2018,}\emph{137}
, 5.

161. Galano, A.; Perez-Gonzalez, A.; Castaneda-Arriaga, R.;
Munoz-Rugeles, L.; Mendoza-Sarmiento, G.; Romero-Silva, A.;
Ibarra-Escutia, A.; Rebollar-Zepeda, A. M.; Leon-Carmona, J. R.;
Hernandez-Olivares, M. A., et al., Empirically fitted parameters for
calculating pkavalues with small deviations from experiments using a
simple computational strategy. \emph{J. Chem. Inf. Model.}\textbf{2016,}
\emph{56} , 1714.

162. Zhong, H.; Mashinson, V.; Woolman, T.; Zha, M., Understanding the
molecular properties and metabolism of top prescribed drugs. \emph{Curr.
Top. Med. Chem.} \textbf{2013,} \emph{13} , 1290.

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