Transfer Learning Enables Drug-Target Interaction Prediction in Data-Scarce One-Carbon Metabolism

Predicting drug-target interactions (DTIs) with deep learning offers opportunities to accelerate drug discovery, yet performance is constrained by the scarcity of target-specific training data. This is a particular challenge for mitochondrial one-carbon (1C) pathway enzymes, which are attractive therapeutic targets but remain pharmacologically understudied. Mitochondrial 1C metabolism supplies glycine, reducing equivalents, and 1C units critical for nucleotide synthesis, and has emerged as a key pathway in cancer and fibrosis. SHMT2 and MTHFD2, two key 1C enzymes, support collagen production in fibroblasts, blocking either prevents TGF-{beta}-induced glycine and collagen accumulation. Here, we developed transfer learning-based deep learning models to predict interactions between approved drugs and SHMT2 or MTHFD2 despite minimal target-specific training data, pre-training on large datasets from related enzymes before fine-tuning to these targets. Virtual screening of the DrugBank library identified six candidates, three of which, Carbimazole, Crizotinib, and GSK2018682 reduced TGF-{beta}-induced collagen production and glycine accumulation in human lung fibroblasts, demonstrating transfer learning as a strategy for repurposable drug identification in data-scarce metabolic targets.