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タイトル: Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples
著者: Koyama, Takuto
Matsumoto, Shigeyuki  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-9329-6362 (unconfirmed)
Iwata, Hiroaki  KAKEN_id
Kojima, Ryosuke  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-1095-8864 (unconfirmed)
Okuno, Yasushi
著者名の別形: 小山, 拓豊
松本, 篤幸
岩田, 浩明
小島, 諒介
奥野, 恭史
キーワード: Algorithms
Drug discovery
Peptides and proteins
Receptors
Students
発行日: 14-Aug-2023
出版者: American Chemical Society (ACS)
誌名: Journal of Chemical Information and Modeling
巻: 63
号: 15
開始ページ: 4552
終了ページ: 4559
抄録: Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.
著作権等: © 2022 The Authors. Published by American Chemical Society
This publication is licensed under CC-BY-NC-ND 4.0.
URI: http://hdl.handle.net/2433/285076
DOI(出版社版): 10.1021/acs.jcim.3c00269
PubMed ID: 37460105
出現コレクション:学術雑誌掲載論文等

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