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Improving Molecular Design by Stochastic Iterative Target Augmentation

preprint
submitted on 11.02.2020 and posted on 12.02.2020 by Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S Jaakkola
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the lack of sufficient training data. In this paper, we propose a surprisingly effective self-training approach for iteratively creating additional molecular targets. We first pre-train the generative model together with a simple property predictor. The property predictor is then used as a likelihood model for filtering candidate structures from the generative model. Additional targets are iteratively produced and used in the course of stochastic EM iterations to maximize the log-likelihood that the candidate structures are accepted. A simple rejection (re-weighting) sampler suffices to draw posterior samples since the generative model is already reasonable after pre-training. We demonstrate significant gains over strong baselines for both unconditional and conditional molecular design. In particular, our approach outperforms the previous state-of-the-art in conditional molecular design by over 10% in absolute gain.

History

Email Address of Submitting Author

yangk@berkeley.edu

Institution

UC Berkeley

Country

United States

ORCID For Submitting Author

0000-0001-7221-6642

Declaration of Conflict of Interest

No conflicts to declare.

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