Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences

Mutation-induced drug resistance challenges both pandemic surveillance and drug discovery. While experimental assays are resource-intensive, current computational predictions remain limited by the scarcity of 3D mutant protein structures. We present DeepMutDTA, a structure-independent model pre-trained on 1.5 million data points to predict drug-target affinity and uncover underlying interaction mechanisms. However, like other sequence-based approaches, it often falls short in predicting mutant affinities due to the overwhelming sequence similarity between wild-type (WT) and mutant (MT) targets. To bridge this gap, we introduce SimSiam-MuTF, a novel fine-tuning framework to enhance the detection of resistance variants by explicitly aligning latent embedding distances with the corresponding shifts in binding affinity between WT and MT targets. Compared to representative baselines, our model exhibits remarkable robustness across varied sequence identities and unseen data splits, yielding average performance gains of 2.47% (PCC) and 5.10% (SCC) in regression tasks, alongside 4.00% (AUC) and 4.17% (AUPR) in classification tasks. Applications to SARS-CoV-2, HIV-1, and cancer-related targets highlight its generalization potential and utility in informing therapeutic strategies against drug resistance. Collectively, this robust computational pipeline and fine-tuning framework deepen our understanding of mutation-induced resistance and may serve as a powerful platform to accelerate drug discovery against mutant targets.