MTS-DTA: A drug target affinity prediction framework based on multi-task optimization and co-training

Drug-target affinity DTA prediction remains a critical challenge in AI-driven drug discovery yet suffers from severe scarcity of experimentally validated data due to the prohibitively high costs and time-intensive nature of biochemical as-says. This data limitation not only amplifies overfitting risks but also compromis-es model generalizability under real-world distributional shifts. While existing approaches predominantly rely on molecular docking simulations and generative models�capable of producing synthetic data�they inadequately exploit available information due to inherent prior biases. To address these challenges, we propose MTS-DTA, a semi-supervised multi-task framework integrating co-training strat-egies with cross-task representation alignment. The framework introduces two core innovations: 1 multi-task synchronization, which enhances feature general-izability through joint optimization of representation and prediction tasks 2 cor-relation-guided pseudo-labeling, dynamically generating pseudo-labels via inter-task dependencies to leverage unlabeled data while mitigating noise propagation. Benchmark evaluations confirm the framework�s improved robustness against distributional biases, establishing a viable strategy to address data scarcity in drug discovery.