BitterTranslate: A Natural Language Processing and Machine Learning-based Framework for Mapping Bitter Taste Receptor Agonism

Bitter taste receptors (TAS2Rs) are G protein-coupled receptors (GPCRs) expressed on the tongue and by many extraoral tissues. Identifying TAS2R ligands is an area of interest for improving bitter-drug compliance, treating various illnesses, and studying the receptors’ extraoral functions. Although machine learning, emerging as a promising tool for drug discovery, can in theory be used for predicting TAS2R activators, obtaining high-quality features from which the machine learning model can learn is time-intensive and reliant on specialized software. This work explores the potential of transformers (a neural network architecture that has revolutionized natural language processing-based tasks and is a powerful tool for extracting features from sequential biomolecular and chemical data) in computer-aided drug design, specifically for predicting potential ligands for TAS2R activation. We developed BitterTranslate, a screening algorithm to predict TAS2R agonists based solely on a Simplified Molecular-Input Line-Entry System (SMILES) string of the ligand and the amino acid sequence of the TAS2R. Bidirectional Encoder Representations from Transformers (BERT) models trained on small molecules and GPCRs were used to extract ligand and receptor features. An XGBoost classifier was pre-trained on a large GPCR–ligand dataset and fine-tuned on the smaller TAS2R–ligand dataset. The algorithm predicts ligand associations with an 80% precision and 65% recall across all TAS2Rs and an 83% precision and 88% recall for the top receptor, TAS2R14. Since BitterTranslate performs reasonably well for TAS2Rs for which the data is scarce, it can be expected to perform even better for other more populated families of GPCRs with more ligand information available.