Improving Molecular Design by Stochastic Iterative Target Augmentation

By Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola. 2020

31 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Natural Language Processing

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Citations in the corpus

Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, methodology): “This is commonly used in structured generation problems such as semantic parsing, program synthesis and molecule generation (Liang et al., 2013; Berant et al., 2013; Guu et al., 2017; Min et al., 2019; Zhong et al., 2020; Yang et al., 2020).”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, methodology): “Given a ﬁxed predictor f , we use the ITA algorithm to ﬁnetune our pretrained models to generate neutralizing antibodies.”