<p>In medicinal chemistry
programs it is key to design and make compounds that are efficacious and safe.
This is a long, complex and difficult multi-parameter optimization process,
often including several properties with orthogonal trends. New methods for the
automated design of compounds against profiles of multiple properties are thus
of great value. Here we present a fragment-based reinforcement learning
approach based on an actor-critic model, for the generation of novel molecules
with optimal properties. The actor and the critic are both modelled with
bidirectional long short-term memory (LSTM) networks. The AI method learns how
to generate new compounds with desired properties by starting from an initial
set of lead molecules and then improve these by replacing some of their
fragments. A balanced binary tree based on the similarity of fragments is used
in the generative process to bias the output towards structurally similar
molecules. The method is demonstrated by a case study showing that 93% of the
generated molecules are chemically valid, and a third satisfy the targeted
objectives, while there were none in the initial set.</p>
Reinforcement Learning Meets Hybrid Zero Dynamics: A Case Study for RABBIT
