Illuminating elite patches of chemical space.

Chemical Science
Jonas Verhellen, Jeriek Van den Abeele

Abstract

In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [J. H. Jensen, Chem. Sci., 2019, 10, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [J. B. Mouret and J. Clune, Proceedings of the Artificial Life Conference, 2012, pp. 593-594], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

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Citations

Jun 4, 2021·Drug Discovery Today·Joshua MeyersNathan Brown

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Software Mentioned

Elites
COVID Moonshot
ZINC
SMILES LSTM
GuacaMol
MAP
GA
SMILES
GB
EPI

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