Deep reinforcement learning for de novo drug design

TLDR

The authors introduce ReLeaSE, a reinforcement‑learning framework for de novo molecular design targeting desired properties. ReLeaSE employs a stack‑augmented memory generative network and a predictive network trained on SMILES strings, first learning separately via supervised learning and then jointly via reinforcement learning to steer generation toward compounds with specified physical or biological properties. Proof‑of‑concept experiments demonstrate that ReLeaSE can generate targeted libraries biased toward structural complexity, specific physical property ranges, and novel JAK2 inhibitors, indicating its utility for multi‑property optimization.

Abstract

We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately but employed jointly to generate novel targeted chemical libraries. ReLeaSE employs simple representation of molecules by their SMILES strings only. Generative models are trained with stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the reinforcement learning approach to bias the generation of new chemical structures towards those with the desired physical and/or biological properties. In the proof-of-concept study, we have employed the ReLeaSE method to design chemical libraries with a bias toward structural complexity or biased toward compounds with either maximal, minimal, or specific range of physical properties such as melting point or hydrophobicity, as well as to develop novel putative inhibitors of JAK2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.