Issue 5, 2024

A physics-inspired approach to the understanding of molecular representations and models

Abstract

The story of machine learning in general, and its application to molecular design in particular, has been a tale of evolving representations of data. Understanding the implications of the use of a particular representation – including the existence of so-called ‘activity cliffs’ for cheminformatics models – is the key to their successful use for molecular discovery. In this work we present a physics-inspired methodology which exploits analogies between model response surfaces and energy landscapes to richly describe the relationship between the representation and the model. From these similarities, a metric emerges which is analogous to the commonly used frustration metric from the chemical physics community. This new property shows state-of-the-art prediction of model error, whilst belonging to a novel class of roughness measure that extends beyond the known data allowing the trivial identification of activity cliffs even in the absence of related training or evaluation data.

Graphical abstract: A physics-inspired approach to the understanding of molecular representations and models

Article information

Article type
Paper
Submitted
07 12 2023
Accepted
22 2 2024
First published
01 3 2024
This article is Open Access
Creative Commons BY license

Mol. Syst. Des. Eng., 2024,9, 449-455

A physics-inspired approach to the understanding of molecular representations and models

L. Dicks, D. E. Graff, K. E. Jordan, C. W. Coley and E. O. Pyzer-Knapp, Mol. Syst. Des. Eng., 2024, 9, 449 DOI: 10.1039/D3ME00189J

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