Title: Deep Reinforcement Learning for De-Novo Drug Design,
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.