Compound Cocrystal Prediction via Dual-View Learning Framework Under Adversarial Consistency and Complementarity Constraints

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.