Objective To tackle the problems of inaccurate prediction of molecular activity values and low generalizability in current drug R&D a multi-channel semantic deep neural network based on the combination of knowledge prior and a m tt o e l n ec ti u o l n es m e t c h h i a s n n is e m two is rk pr c o a p n os p e r d e . dic B t y th u e sin p g IC t 5 h 0 e b si i m oa p c l t i i f v ie e d v m al o u l e e s cu o l f ar es i t n ro p g u e t n li r n e e ce e p n t t o r r y α sys is te o m form S M E I R L α E S . M ex e p t r h e o ss d i s on T s h o is f network adopted a two-stage feature extraction strategy. At the semantic layer a semantic analysis network that combined knowledge prior and transfer learning was designed. It located the key information of molecular SMILES descriptors and graph representations. By fine-tuning the parameters in the Erα dataset comprehensive molecular SMILES representation information was obtained. At the channel layer based on the efficient channel attention ECA mechanism a 1D-ECA algorithm was designed and embedded into the CNN sub-module. This formed a multi-channel deep neural 1D-ECA-CNN module which re-extracted the features of molecular representation and reduced the information loss during the molecular representation learning process. Finally by combining the semantic layer and the channel layer a Knowledge-BERT-1DECA-CNN KBAC deep neural network was formed to achieve the regression prediction of the pIC50 biological activity value. Results Experimental results demonstrated the superior performance of the proposed framework across four evaluation metrics. The proposed network achieved a mean absolute error MAE of 0. 091 a mean squared error MSE of 0. 014 a root mean squared error RMSE of 0. 117 and a coefficient of determination R 2 of 0. 993. These results represent a significant improvement over four representative baseline models indicating higher prediction accuracy of the proposed network. Conclusion The two-stage feature extraction process enables the acquisition of more comprehensive molecular features facilitating the screening of candidate drugs for treating diseases.