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Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery : From Small Molecules to Biologics
https://repository.lib.tottori-u.ac.jp/records/2002099
https://repository.lib.tottori-u.ac.jp/records/20020998ff039e9-1bf7-4a5b-8ac2-5e72c93b0169
| 名前 / ファイル | ライセンス | アクション |
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yam69(1)_1.pdf (1.4 MB)
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© 2026 Tottori University Medical Press
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| アイテムタイプ | デフォルトアイテムタイプ（フル）(1) | |||||||||||||
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| 公開日 | 2026-03-09 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery : From Small Molecules to Biologics | |||||||||||||
| 言語 | en | |||||||||||||
| 作成者 |
Tanihata,Soyoka
× Tanihata,Soyoka
× 岩田,浩明
研究者総覧鳥取大学
100002714_ja.html
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| 主題 | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | ADME prediction | |||||||||||||
| 主題 | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | artificial intelligence | |||||||||||||
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| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | biologics | |||||||||||||
| 主題 | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | peptides | |||||||||||||
| 主題 | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | small molecules | |||||||||||||
| 内容記述 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | Pharmacokinetic (PK) behavior, which emerges from the underlying processes of absorption, distribution, metabolism, and excretion (ADME), is central to drug discovery and development, dose optimization, and safety assessment. Despite decades of experimental and computational research, early-stage prediction of human PK remains a major challenge, contributing to clinical attrition and inefficiency in pharmaceutical pipelines. Advances in artificial intelligence (AI) and machine learning (ML) have significantly improved ADME predictions, particularly for small molecules. Traditional descriptor-based quantitative structure–activity relationship and classical ML methods offer interpretability and robust performance on standardized datasets. In contrast, graph neural networks, deep learning architectures, and chemical language models facilitate the learning of complex nonlinear structure–property relationships and multitask predictions. Multimodal frameworks further integrate experimental measurements, structural data, and biological contexts, enhancing predictive accuracy under low-data and heterogeneous conditions. Emerging modalities, including peptides, oligonucleotides, and antibody-based therapeutics, pose additional challenges owing to their sequence-dependent stability, conformational flexibility, and mechanistically distinct determinants of ADME and toxicity (ADMET). AI approaches that incorporate sequence-, structure-, and mechanism-aware representations combined with multimodal data integration have demonstrated improved predictability for medium- and large-molecule therapeutics. Recent developments in foundation-model architectures offer unified representations across chemical, biological, and biophysical domains, enabling cross-modality ADMET modeling with enhanced generalization and mechanistic interpretability. In this review, we summarize the evolution of computational ADME- and PK-oriented prediction frameworks from small molecules to complex biologics, highlighting methodological advances, representative studies, and emerging trends in multimodal and foundation-model approaches. We also discuss the limitations and future perspectives of the practical implementation of AI-driven ADMET predictions to support rational drug design and development. | |||||||||||||
| 言語 | en | |||||||||||||
| 出版者 | ||||||||||||||
| 出版者 | Tottori University Medical Press | |||||||||||||
| 言語 | en | |||||||||||||
| 日付 | ||||||||||||||
| 日付 | 2026-02-19 | |||||||||||||
| 日付タイプ | Issued | |||||||||||||
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| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 関連情報 | ||||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.33160/yam.2026.02.001 | |||||||||||||
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| 関連タイプ | isIdenticalTo | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
| 関連識別子 | https://www.lib.tottori-u.ac.jp/yam/yam/yam69-1/69-1contents.html | |||||||||||||
| 収録物識別子 | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 13468049 | |||||||||||||
| 収録物識別子 | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AA00892882 | |||||||||||||
| 書誌情報 |
en : Yonago Acta Medica 巻 69, 号 1, p. 1-13, ページ数 13, 発行日 2026-02-19 |
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| 見出し | ||||||||||||||
| 大見出し | Review Article : Special Contribution | |||||||||||||
| 言語 | en | |||||||||||||
| 出版者情報 | ||||||||||||||
| 出版者名 | Tottori University Medical Press | |||||||||||||
| 言語 | en | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||
| 権利情報 | ||||||||||||||
| 権利情報 | © 2026 Tottori University Medical Press | |||||||||||||
| 言語 | en | |||||||||||||
