摘要
Abstract
Drug-target binding affinity is a key metric for evaluating the strength of interaction between drugs and their targets.Currently,most drug-target affinity prediction methods focus on single-modal features of either the drug or the target,failing to fully exploit the complementary nature of multi-modal information and its potential value in enhancing prediction performance.To address this issue,we propose a drug-target affinity prediction model based on multi-modal feature fusion(MMF-DTA).The model incorporates multi-modal information for drug molecules,including molecular fingerprints,molecular graphs,and ChemBERTa pre-trained embeddings,and for target proteins,it uses protein sequences,amino acid residue contact maps,and ProtBERT pre-trained embeddings.Based on these features,the model adopts a hierarchical feature fusion architecture to enable deep interaction and fusion between the drug and target multi-modal features.Experimental results demonstrate that our model outperforms other baseline methods on the Davis and KIBA datasets,validating the effectiveness of the proposed multi-modal fusion strategy.关键词
药物靶标亲和力预测/药物研发/多模态/特征融合/图神经网络Key words
drug-target affinity prediction/drug development/multimodal/feature fusion/graph neural network分类
信息技术与安全科学
