What is it about?
It is a vital step to find cocrystal formers of drugs in drug development. To address these issues, this work elaborates a novel Dual-View Learning 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 both an elaborate 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's anticipated that this work can boost drug development.
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Why is it important?
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.
Perspectives
It has been a great pleasure to write this article, as it involves co-authors with whom I have had long-term collaborations. I hope this work contributes to future discoveries of drug cocrystals.
haoyang yu
Read the Original
This page is a summary of: DVL-CC: A Novel Dual-View Learning Framework for Compound Cocrystal Prediction Boosted by View Consistency and Complementarity, November 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3698587.3701355.
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