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.

The interactions between drugs and microbes affecting microbial abundance can lead to various diseases or reduce the effectiveness of pharmaceutical treatments. Traditional Microbe-Drug Association (MDA) determination through biological assays is time-consuming and costly. With the accumulation of MDA data, computational methods have become a promising approach to infer potential MDAs. Although existing methods focus on predicting whether a drug interacts with a microbe, they can rarely infer whether a drug promotes or inhibits the abundance of a given microbe. Moreover, the extreme imbalance among abundance-promoted, abundance-inhibited, and non-impacted cases remains a challenge for computational prediction methods. To address these issues, we propose a framework for predicting the imbalanced Impact of Drugs on Microbial Abundance by leveraging Multi-view Learning and Data Augmentation, named IDMA-MLDA. IDMA-MLDA employs a novel method of transforming a bipartite graph into a hypergraph, uses hypergraph convolutions to capture high-order vertex neighborhoods (macro-view), and employs graph neural networks to learn individual features of drugs and microbes (micro-view). It integrates features from both macro-view and micro-view to obtain more comprehensive representations, incorporates a data augmentation module to handle class imbalance, and uses a multilayer perceptron to predict the impact of drugs on microbial abundance. We demonstrate the superiority of IDMA-MLDA through comparisons with six baseline methods, and ablation studies affirm the contributions of each key module in IDMA-MLDA’s prediction. Furthermore, a comprehensive literature review verifies the abundance types of twelve MDAs predicted by IDMA-MLDA.