A Model to Search for Synthesizable Molecules

Abstract

Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecule generation model, mirroring a more realistic real-world process, where (a) reactants are selected, and (b) combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. We first show that the model can generate diverse, valid and unique molecules due to the useful inductive biases of modeling reactions. Furthermore, our model allows chemists to interrogate not only the properties of the generated molecules but also the feasibility of the synthesis routes. We conclude by using our model to solve retrosynthesis problems, predicting a set of reactants that can produce a target product.

arXiv commentTo appear in Advances in Neural Information Processing Systems 2019

References in corpus (13)

• Auto-Encoding Variational Bayes
• Practical Bayesian Optimization of Machine Learning Algorithms
• Learning to Plan Chemical Syntheses
• Junction Tree Variational Autoencoder for Molecular Graph Generation
• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
• MolGAN: An implicit generative model for small molecular graphs
• Learning Deep Generative Models of Graphs
• Constrained Graph Variational Autoencoders for Molecule Design
• Syntax-Directed Variational Autoencoder for Structured Data
• Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
• DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
• Molecular Hypergraph Grammar with its Application to Molecular Optimization

Cited by in corpus (4)

• Autonomous discovery in the chemical sciences part II: Outlook
• Generative Models for Automatic Chemical Design
• Constrained Bayesian Optimization for Automatic Chemical Design
• A generative model for molecule generation based on chemical reaction trees