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Fragment-Based Sequential Translation for Molecular Optimization
Benson Chen · Xiang Fu · Regina Barzilay · Tommi Jaakkola

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in AI for Science: Mind the Gaps »
Event URL: https://openreview.net/forum?id=E_Slr0JVvuC »

Search of novel molecular compounds with desired properties is an important problem in drug discovery. Many existing generative models for molecules operate on the atom level. We instead focus on generating molecular fragments--meaningful substructures of molecules. We construct a coherent latent representation for molecular fragments through a learned variational autoencoder (VAE) that is capable of generating diverse and meaningful fragments. Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which iteratively translates model-discovered molecules into increasingly novel molecules with high property scores. Empirical evaluation shows that FaST achieves significant improvement over state-of-the-art methods on benchmark single-objective/multi-objective molecular optimization tasks.

Author Information

Benson Chen (Massachusetts Institute of Technology)
Xiang Fu (MIT)
Regina Barzilay (Massachusetts Institute of Technology)
Tommi Jaakkola (MIT)

Tommi Jaakkola is a professor of Electrical Engineering and Computer Science at MIT. He received an M.Sc. degree in theoretical physics from Helsinki University of Technology, and Ph.D. from MIT in computational neuroscience. Following a Sloan postdoctoral fellowship in computational molecular biology, he joined the MIT faculty in 1998. His research interests include statistical inference, graphical models, and large scale modern estimation problems with predominantly incomplete data.

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