Predicting Binding from Screening Assays with Transformer Network Embeddings.

Cheminformatics aims to assist in chemistry applications that depend on molecular interactions, structural characteristics, and functional properties. The arrival of deep learning and the abundance of easily accessible chemical data from repositories like PubChem have enabled advancements in computer-aided drug discovery. Virtual high-throughput screening (vHTS) is one such technique that integrates chemical domain knowledge to perform in silico biomolecular simulations, but prediction of binding affinity is restricted due to limited availability of ground-truth binding assay results. Here, text representations of 83 000 000 molecules are leveraged to perform single-target binding affinity prediction directly on the outcome of screening assays. The embedding of an end-to-end transformer neural network, trained to encode the structural characteristics of a molecule via a text-based translation task, is repurposed through transfer learning to classify binding affinity to single targets with few known binding compounds. We quantify the observed increase in AUC on binding prediction tasks between classifiers trained on the translation embedding versus those using an untrained embedding. Visualization of the embedding space reveals organization of structural and functional properties that aid binding prediction. The pretrained transformer, data, and associated software to extract embeddings are made publicly available at https://github.com/mpcrlab/MolecularTransformerEmbeddings.