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Chemical design is often complex, requiring the optimal trade-off between several competing objectives. Multi-objective optimization algorithms are designed to optimally balance multiple objectives, but many chemical design approaches use the naïve weighted sum method, which is not guaranteed to give desired solutions. Here, we rigorously assess the performance of genetic algorithms for inverse molecular design, using more advanced multi-objective methods. Chimera and Hypervolume are assessed against relevant baselines for the optimization of molecules with high logP and high QED score. As a more realistic task, we also simulate a drug design campaign, optimizing for synthetically accessible molecules which bind to the 1OYT protein. We show that both methods achieve better formal optimality than the baselines and generate molecules closer to a user-specified Utopian point in property space, mimicking typical materials design objectives.
Author Information
Nathanael Kusanda (University of Toronto)
I am a third-year Energy Systems Engineering student passionate about accelerating the transition to Renewable Energy through Artificial Intelligence. I produced a paper for the AI4Mat workshop benchmarking my implementation of Multi-objective Optimisation in Genetic Algorithms, which I worked on with the Aspuru-Guzik group to complement a project using AI to generate molecules for Organic Redox Flow Batteries.
Gary Tom (University of Toronto)
Riley Hickman (University of Toronto)
AkshatKumar Nigam (University Of Toronto)
Kjell Jorner
Alan Aspuru-Guzik (University of Toronto)
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