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Predicting Residence Time of GPCR Ligands with Machine Learning

Potterton, A; Heifetz, A; Townsend-Nicholson, A; (2022) Predicting Residence Time of GPCR Ligands with Machine Learning. Methods in Molecular Biology , 2390 pp. 191-205. 10.1007/978-1-0716-1787-8_8. Green open access

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Abstract

Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug discovery, machine learning models that can predict that value need to be developed. One of the main challenges with predicting residence time is the paucity of data. This chapter outlines all of the currently available ligand kinetic data, providing a repository that contains the largest publicly available source of GPCR-ligand kinetic data to date. To help decipher the features of kinetic data that might be beneficial to include in computational models for the prediction of residence time, the experimental evidence for properties that influence residence time are summarized. Finally, two different workflows for predicting residence time with machine learning are outlined. The first is a single-target model trained on ligand features; the second is a multi-target model trained on features generated from molecular dynamics simulations.

Type: Article
Title: Predicting Residence Time of GPCR Ligands with Machine Learning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-1-0716-1787-8_8
Publisher version: https://doi.org/10.1007/978-1-0716-1787-8_8
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Residence time, Machine learning, Drug discovery, GPCR, Binding kinetics, Molecular dynamics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/10139571
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