230 papers with code • 27 benchmarks • 22 datasets
Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: A Turing Test for Molecular Generators )
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.
Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.