It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e....It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e.,sequence and 2D structures).Nonetheless,it is still an ongoing issue that the performance of existing DVL-based approaches depend on how appropriate the combination of dual view is.Furthermore,there is a need to elucidate what atoms are crucial to form a cocrystal of two compounds.This work holds an assumption that the orthogonal separation of view representations into view-shared representations and view-specific representations can eliminate the redundancy and irrelevant features among dual view.To address these issues,this work elaborates a novel DVL framework for predicting Compound Cocrystal(DVL-CC).The framework includes molecule encoders of dual view,a dual-view combinator,and a binary predictor.Especially,the dual-view combinator orthogonally disentangles view-shared and view-specific molecule representations from raw view representations by an elaborate Generative Adversarial Network(GAN)based consistency learner and a set of complementary constraints.The comparison with state-of-the-art DVL-based methods demonstrates the superiority of DVL-CC.Also,the comprehensive ablation studies validate and illustrate how its main components contribute to the cocrystal prediction,including individual-view representations,the dual-view combinator,the consistency learner,and the complementary constraints.Furthermore,a case study illustrates the interpretability of DVL-CC by indicating crucial atoms associated with cocrystal conformation patterns between compounds.It is anticipated that this work can boost drug development.The code and data underlying this article are available at https://github.com/savior-22/DVL-CC.展开更多
基金supported by the National Natural Science Foundation of China(No.62372375)and the Shaanxi Province Key R&D Program(No.2023-YBSF-114).
文摘It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e.,sequence and 2D structures).Nonetheless,it is still an ongoing issue that the performance of existing DVL-based approaches depend on how appropriate the combination of dual view is.Furthermore,there is a need to elucidate what atoms are crucial to form a cocrystal of two compounds.This work holds an assumption that the orthogonal separation of view representations into view-shared representations and view-specific representations can eliminate the redundancy and irrelevant features among dual view.To address these issues,this work elaborates a novel DVL framework for predicting Compound Cocrystal(DVL-CC).The framework includes molecule encoders of dual view,a dual-view combinator,and a binary predictor.Especially,the dual-view combinator orthogonally disentangles view-shared and view-specific molecule representations from raw view representations by an elaborate Generative Adversarial Network(GAN)based consistency learner and a set of complementary constraints.The comparison with state-of-the-art DVL-based methods demonstrates the superiority of DVL-CC.Also,the comprehensive ablation studies validate and illustrate how its main components contribute to the cocrystal prediction,including individual-view representations,the dual-view combinator,the consistency learner,and the complementary constraints.Furthermore,a case study illustrates the interpretability of DVL-CC by indicating crucial atoms associated with cocrystal conformation patterns between compounds.It is anticipated that this work can boost drug development.The code and data underlying this article are available at https://github.com/savior-22/DVL-CC.
