Multi-order graph attention network for water solubility prediction and interpretation

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reduc...

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Bibliographic Details
Published in:Scientific reports 2023-03, Vol.13 (1), p.957-957, Article 957
Main Authors: Lee, Sangho, Park, Hyunwoo, Choi, Chihyeon, Kim, Wonjoon, Kim, Ki Kang, Han, Young-Kyu, Kang, Joohoon, Kang, Chang-Jong, Son, Youngdoo
Format: Article
Language:English
Subjects:
631/114/1305
639/638/630
639/705/117
639/705/258
Computer applications
Embedding
Graph representations
Humanities and Social Sciences
Learning algorithms
Machine learning
Medical research
multidisciplinary
Predictions
Science
Science (multidisciplinary)
Solubility
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Summary:The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-25701-5