ResGIN-Att predicts drug synergy by extracting multi-scale molecular features with residual GIN, fusing them via LSTM, and modeling interactions with cross-attention, achieving competitive results on five benchmark datasets.
Matchmaker: a deep learning framework for drug synergy prediction.IEEE/ACM transactions on computational bi- ology and bioinformatics, 19(4):2334–2344
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Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms
ResGIN-Att predicts drug synergy by extracting multi-scale molecular features with residual GIN, fusing them via LSTM, and modeling interactions with cross-attention, achieving competitive results on five benchmark datasets.