DockStream: a docking wrapper to enhance de novo molecular design

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 obstac...

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Bibliographic Details
Published in:Journal of cheminformatics Vol. 13; no. 1; pp. 89 - 21
Main Authors: Guo, Jeff, Janet, Jon Paul, Bauer, Matthias R., Nittinger, Eva, Giblin, Kathryn A., Papadopoulos, Kostas, Voronov, Alexey, Patronov, Atanas, Engkvist, Ola, Margreitter, Christian
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 17.11.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Benchmarks
Binding sites
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computer Applications in Chemistry
Configurations
De novo design
Documentation and Information in Chemistry
Drug discovery
Generative Models
Iterative methods
Molecular docking
Reinforcement Learning (RL)
Research Article
Structure-activity relationships
Structure-based drug discovery (SBDD)
Theoretical and Computational Chemistry
Workflow
Generative Models
Reinforcement Learning (RL)
Structure-based drug discovery (SBDD)
De novo design
Molecular docking
ISSN:1758-2946, 1758-2946
Online Access:Get full text
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Summary: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 .
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-021-00563-7