Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins

Abstract

Machine learning models that predict which small molecule ligands bind a single protein target report high levels of accuracy for held-out test data. An important challenge is to extrapolate and make accurate predictions for new protein targets. Improvements in drug-target interaction (DTI) models that address this challenge would have significant impact on drug discovery by eliminating the need for high-throughput screening experiments against new protein targets. Here we propose a data augmentation strategy that addresses this challenge to enable accurate prediction in cases where no experimental data is available. To proceed, we first build single protein-ligand binding models and use these models to predict whether additional ligands bind to each protein. We then use these predictions to augment the experimental data, train standard DTI models, and predict interactions between unseen test proteins and ligands. This approach achieves Area Under the Receiver Operator Characteristic (AUC) > 0.9 consistently on test sets consisting exclusively of proteins and ligands for which the model is given no experimental data. We verify that performance improvements extend to held-out test proteins distant from the training set. Our data augmentation framework can be applied to any DTI model, and enhances performance on a range of simple models.

Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development

“…SmoteR) and proposing evaluation metrics that take varying importance of observations into consideration [ 17 , 18 ]. A recent study proposed another straightforward strategy to rise to the challenge, which labels additional training data points using established prediction models, then trains a standard prediction model with augmented datasets [ 19 ]. All these studies emphasize data that are more balanced would enhance the generalization ability of the CPI model.…”