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Assessing Methods and Obstacles in Chemical Space Exploration
  • +3
  • Shawn Reeves,
  • Benjamin DiFrancesco,
  • Vijay Shahani,
  • Stephen MacKinnon,
  • Andreas Windemuth,
  • Andrew Brereton
Shawn Reeves
Cyclica, Inc.
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Benjamin DiFrancesco
Cyclica, Inc.
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Vijay Shahani
Cyclica, Inc.
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Stephen MacKinnon
Cyclica, Inc.
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Andreas Windemuth
Cyclica, Inc.
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Andrew Brereton
Cyclica, Inc.
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Abstract

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design taks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns.

Peer review status:IN REVISION

17 Aug 2020Submitted to Applied AI Letters
17 Aug 2020Assigned to Editor
17 Aug 2020Submission Checks Completed
19 Aug 2020Reviewer(s) Assigned
21 Sep 2020Review(s) Completed, Editorial Evaluation Pending
22 Sep 2020Editorial Decision: Revise Minor