Issue 2, 2025

Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease

Abstract

FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated de novo design. We interface the workflow with active learning to improve the efficiency of searching the combinatorial space of possible linkers and functional groups, make use of interactions formed by crystallographic fragments in scoring compound designs, and introduce the option to seed the chemical space with molecules available from on-demand chemical libraries. As a test case, we target the main protease (Mpro) of SARS-CoV-2, identifying several small molecules with high similarity to molecules discovered by the COVID moonshot effort, using only structural information from a fragment screen in a fully automated fashion. Finally, we order and test 19 compound designs, of which three show weak activity in a fluorescence-based Mpro assay, but work is needed to further optimise the prioritisation of compounds for purchase. The FEgrow package and full tutorials demonstrating the active learning workflow are available at https://github.com/cole-group/FEgrow.

Graphical abstract: Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease

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Article information

Article type
Paper
Submitted
25 Oct 2024
Accepted
08 Jan 2025
First published
08 Jan 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025,4, 438-450

Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease

B. Cree, M. K. Bieniek, S. Amin, A. Kawamura and D. J. Cole, Digital Discovery, 2025, 4, 438 DOI: 10.1039/D4DD00343H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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