DockStream: a docking wrapper to enhance de novo molecular design

Journal of Cheminformatics

Jon Paul Janet¹  Kathryn A. Giblin²  Eva Nittinger³  Jeff Guo⁴  Alexey Voronov⁴  Kostas Papadopoulos⁴  Christian Margreitter⁴  Atanas Patronov⁴  Ola Engkvist⁵  Matthias R. Bauer⁶ 

[1] Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden;Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK;Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden;Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden;Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden;Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden;Structure & Biophysics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK;

关键词: De novo design;    Generative Models;    Reinforcement Learning (RL);    Molecular docking;    Structure-based drug discovery (SBDD);   

DOI  :  10.1186/s13321-021-00563-7

来源: Springer

【 摘 要 】

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream.

【 授权许可】

CC BY