Fragment-based Sequential Translation for Molecular Optimization

• URL: http://arxiv.org/abs/2111.01009v1
• Date: Tue, 26 Oct 2021 21:20:54 GMT
• Title: Fragment-based Sequential Translation for Molecular Optimization
• Authors: Benson Chen, Xiang Fu, Regina Barzilay, Tommi Jaakkola
• Abstract summary: We propose a flexible editing paradigm that generates molecules using learned molecular fragments. We use a variational autoencoder to encode molecular fragments in a coherent latent space. We then utilize as a vocabulary for editing molecules to explore the complex chemical property space.
• Score: 23.152338167332374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instead propose a flexible editing paradigm that generates molecules using learned molecular fragments--meaningful substructures of molecules. To do so, we train a variational autoencoder (VAE) to encode molecular fragments in a coherent latent space, which we then utilize as a vocabulary for editing molecules to explore the complex chemical property space. Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties. Empirical evaluation shows that FaST significantly improves over state-of-the-art methods on benchmark single/multi-objective molecular optimization tasks.

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