DockStream: a docking wrapper to enhance de novo molecular design
Artikel i vetenskaplig tidskrift, 2021

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.

Generative Models

Structure-based drug discovery (SBDD)

Reinforcement Learning (RL)

Molecular docking

De novo design

Författare

Jeff Guo

AstraZeneca AB

Jon Paul Janet

AstraZeneca AB

Matthias Bauer

AstraZeneca AB

Eva Nittinger

AstraZeneca AB

Kathryn A. Giblin

AstraZeneca AB

Kostas Papadopoulos

AstraZeneca AB

Alexey Voronov

AstraZeneca AB

Atanas Patronov

AstraZeneca AB

Ola Engkvist

Chalmers, Data- och informationsteknik

AstraZeneca AB

Christian Margreitter

AstraZeneca AB

Journal of Cheminformatics

1758-2946 (ISSN)

Vol. 13 1 89

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1186/s13321-021-00563-7

Mer information

Senast uppdaterat

2021-11-26