Roughness of Molecular Property Landscapes and Its Impact on Modellability

Aldeghi, Matteo, Graff, David E, Frey, Nathan, Morrone, Joseph A, Pyzer-Knapp, Edward O ORCID: 0000-0002-8232-8282, Jordan, Kirk E and Coley, Connor W (2022) Roughness of Molecular Property Landscapes and Its Impact on Modellability. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 62 (19). pp. 4660-4671. ISSN 1549-9596, 1549-960X

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Official URL: https://doi.org/10.1021/acs.jcim.2c00903

Abstract

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.

Item Type:	Article
Uncontrolled Keywords:	Drug Design, Machine Learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	03 Mar 2023 09:33
Last Modified:	07 Dec 2024 23:29
DOI:	10.1021/acs.jcim.2c00903
Open Access URL:	https://doi.org/10.48550/arXiv.2207.09250
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168720