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Deriver Paper v2.pdf (1.53 MB)
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Assessing Methods and Obstacles in Chemical Space Exploration

preprint
revised on 20.08.2020 and posted on 21.08.2020 by Shawn Reeves, Benjamin DiFrancesco, Vijay Shahani, Stephen S. MacKinnon, Andreas Windemuth, Andrew E. Brereton
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 tasks. 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.

History

Email Address of Submitting Author

andrew@brereton.me

Institution

Cyclica, Inc.

Country

Canada

ORCID For Submitting Author

0000-0002-6716-7177

Declaration of Conflict of Interest

The Authors of this paper declare no conflict of interests.

Version Notes

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