Title: Learning retrosynthetic planning through self-play,
Abstract: The problem of retrosynthetic planning can be framed as one player game, in
which the chemist (or a computer program) works backwards from a molecular
target to simpler starting materials though a series of choices regarding which
reactions to perform. This game is challenging as the combinatorial space of
possible choices is astronomical, and the value of each choice remains
uncertain until the synthesis plan is completed and its cost evaluated. Here,
we address this problem using deep reinforcement learning to identify policies
that make (near) optimal reaction choices during each step of retrosynthetic
planning. Using simulated experience or self-play, we train neural networks to
estimate the expected synthesis cost or value of any given molecule based on a
representation of its molecular structure. We show that learned policies based
on this value network outperform heuristic approaches in synthesizing
unfamiliar molecules from available starting materials using the fewest number
of reactions. We discuss how the learned policies described here can be
incorporated into existing synthesis planning tools and how they can be adapted
to changes in the synthesis cost objective or material availability.