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Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)
preprintrevised on 16.08.2017, 19:27 and posted on 17.08.2017, 19:46 by Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L. Guimaraes, Alan Aspuru-Guzik
Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.
Dr. Anders Fröseth, Fundacion Maria Cristina Masaveu Peterson
- Computational chemistry and modeling