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Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space
In this work, we propose a novel method that combines in silico prediction of molecular properties such as biological activity or pharmacokinetics with an in silico optimization algorithm, namely Particle Swarm Optimization. Our method takes a starting compound as input and proposes new molecules with more desirable (predicted) properties. It navigates a machine-learned continuous representation of a drug-like chemical space guided by a de ned objective function. The objective function combinesdoi:10.26434/chemrxiv.7971101 fatcat:frcde7wmh5aofhygu4ih2qukve