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chemistry_elite_patches (14).pdf (9.83 MB)

Illuminating Elite Patches of Chemical Space

submitted on 03.07.2020, 11:01 and posted on 06.07.2020, 06:06 by Jonas Verhellen, Jeriek Van den Abeele
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 [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], 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.


Email Address of Submitting Author


University of Oslo



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest.


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