An overview of neural networks for drug discovery and the inputs used.

Introduction: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as inputs for artificial neural networks (ANNs) for a long time, while other ways for describing molecules are used only for storing and presenting molecules. With the development of deep learning, variants of ANNs are now able to use different kinds of inputs, which provide researchers with more choices for drug discovery. Areas covered: The authors provide a brief overview of the applications of NNs in drug discovery. Combined with the characteristics of different ways for describing molecules, corresponding methods based on NNs provide new choices for drug discovery, including de novo drug design, ligand-based drug design, and receptor-based drug design. Expert opinion: Various ways for describing molecules can be inputs of NN-based models, and these models achieve satisfactory results in metrics. Although most of the models have not been widely applied and tested in practice, they can be the basis for automatic drug discovery in the future.