A deep-learning view of chemical space designed to facilitate drug discovery
This work addresses the challenge of expediting drug discovery cycles for pharmaceutical researchers, though it appears incremental as it builds on existing machine learning approaches.
The authors tackled the problem of designing small molecules for drug discovery by introducing DESMILES, a deep neural network model that modifies input molecules to inhibit specific receptors, achieving a 77% lower failure rate compared to state-of-the-art models on a dopamine receptor D2 benchmark.
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.