Issue 3, 2024

Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

Abstract

High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.

Graphical abstract: Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

Supplementary files

Article information

Article type
Research Article
Submitted
16 Dec 2023
Accepted
12 Feb 2024
First published
15 Feb 2024
This article is Open Access
Creative Commons BY license

RSC Med. Chem., 2024,15, 1015-1021

Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

W. McCorkindale, M. Filep, N. London, A. A. Lee and E. King-Smith, RSC Med. Chem., 2024, 15, 1015 DOI: 10.1039/D3MD00719G

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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