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Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Chemistry

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

A. Daina and V. Zoete

This research, conducted by Antoine Daina and Vincent Zoete, explores the groundbreaking potential of ligand-based reverse screening to predict macromolecular targets for small molecule drugs. With a machine-learning model achieving over 51% accuracy on external datasets, this study underscores the approach's promise in drug discovery.

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Playback language: English
Abstract
This paper presents a large-scale evaluation of the predictive power of ligand-based reverse screening for inferring macromolecular targets of small molecule drugs. Using a machine-learning model trained on ChEMBL data and tested on a Reaxys dataset of over 300,000 active small molecules, the study demonstrates that the model can predict the correct targets for over 51% of the external molecules. The findings highlight the method's usefulness in various drug discovery applications and emphasize the importance of using large, high-quality, non-overlapping datasets for benchmarking such approaches.
Publisher
Communications Chemistry
Published On
May 09, 2024
Authors
Antoine Daina, Vincent Zoete
Tags
ligand-based reverse screening
machine-learning model
predictive power
drug discovery
macromolecular targets
ChEMBL
Reaxys dataset

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