Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

Abstract

Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery.

References in corpus (13)

• Learning to Plan Chemical Syntheses
• Junction Tree Variational Autoencoder for Molecular Graph Generation
• Deep learning for molecular design - a review of the state of the art
• Autonomous discovery in the chemical sciences part II: Outlook
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
• Generative Models for Automatic Chemical Design
• Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
• ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
• Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
• MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
• Differentiable Scaffolding Tree for Molecular Optimization
• RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
• Barking up the right tree: an approach to search over molecule synthesis DAGs

Cited by in corpus (3)

• Re-evaluating Retrosynthesis Algorithms with Syntheseus
• Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
• A generative model for molecule generation based on chemical reaction trees