A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery

Drug
discovery projects entail cycles of design, synthesis, and
testing that yield a series of chemically related small molecules
whose properties, such as binding affinity to a given target protein,
are progressively tailored to a particular drug discovery goal. The
use of deep-learning technologies could augment the typical practice
of using human intuition in the design cycle, and thereby expedite
drug discovery projects. Here, we present DESMILES, a deep neural
network model that advances the state of the art in machine learning
approaches to molecular design. We applied DESMILES to a previously
published benchmark that assesses the ability of a method to modify
input molecules to inhibit the dopamine receptor D2, and DESMILES
yielded a 77% lower failure rate compared to state-of-the-art models.
To explain the ability of DESMILES to hone molecular properties, we
visualize a layer of the DESMILES network, and further demonstrate
this ability by using DESMILES to tailor the same molecules used in
the D2 benchmark test to dock more potently against seven different
receptors.