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MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction

Interdisciplinary Sciences: Computational Life Sciences
The discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein targets, focusing on binding within protein cavities.
Riqian Hu +5 more
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MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Methods
Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization.
Xiwei Tang, Wanjun Ma, Mengyun Yang, Wenjun Li +3 more
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Prediction of drug-target binding affinity based on deep learning models

Computers in Biology and Medicine
The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development.
Hao, Zhang +4 more
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Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction