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Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

preprint
revised on 16.08.2017 and posted on 17.08.2017 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.

Funding

Dr. Anders Fröseth, Fundacion Maria Cristina Masaveu Peterson

History

Topic

  • Computational chemistry and modeling
  • Theory

Email Address of Submitting Author

bsanchezlengeling@g.harvard.edu

Email Address(es) for Other Author(s)

aspuru@chemistry.harvard.edu

Institution

Harvard University

Country

United States

ORCID For Submitting Author

0000-0002-1116-1745

Declaration of Conflict of Interest

no conflict of interest

Exports