How to Best Represent Proteins in Machine Learning-based Prediction of Drug/Compound-Target Interactions

Abstract

AbstractThe identification of drug/compound-target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been applied. As a relatively new data-driven paradigm, proteochemometric (PCM) modeling utilizes both protein and compound properties as a pair at the input level and processes them via statistical/machine learning. The representation of input samples (i.e., proteins and their ligands) in the form of quantitative feature vectors is crucial for the extraction of interaction-related properties during the artificial learning and subsequent prediction of DTIs. Lately, the representation learning approach, in which input samples are automatically featurized via training and applying a machine/deep learning model, has been utilized in biomedical sciences. In this study, we performed a comprehensive investigation of different computational approaches/techniques for data preparation and protein featurization, including both conventional approaches and the novel learned embeddings, with the aim of achieving better data representations and more successful learning in PCM-based DTI prediction. For this, we first constructed realistic and challenging benchmark datasets on small, medium, and large scales to be used as reliable gold standards for specific DTI modeling tasks. We developed and applied a network analysis-based splitting strategy to divide datasets into structurally different training and test folds. Using these datasets together with various featurization methods, we trained and tested DTI prediction models and evaluated their performance from different angles. Our main findings can be summarized under 3 items: (i) random splitting of the dataset into train and test folds leads to near-complete data memorization and produce highly over-optimistic results, as a result, it should be avoided; (ii) learned protein sequence embeddings works well in DTI prediction, even though no information related to protein structures, interactions or biochemical properties is utilized during the training of these models; and (iii) PCM models tends to learn from compound features and leave out protein features, mostly due to the natural bias in DTI data. We hope this study will aid researchers in designing robust and high-performing data-driven DTI prediction systems that have real-world translational value in drug discovery.