Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development
ABSTRACT Artificial intelligence (AI) is revolutionizing biotechnology by transforming the landscape of therapeutic development. Traditional drug discovery faces persistent challenges, including high attrition rates, billion‐dollar costs, and timelines exceeding a decade. Recent advances in AI—parti...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | MedComm |
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| Online Access: | https://doi.org/10.1002/mco2.70317 |
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| author | Yuan‐Tao Liu Le‐Le Zhang Zi‐Ying Jiang Xian‐Shu Tian Peng‐Lin Li Pei‐Huang Wu Wen‐Ting Du Bo‐Yu Yuan Chu Xie Guo‐Long Bu Lan‐Yi Zhong Yan‐Lin Yang Ting Li Mu‐Sheng Zeng Cong Sun |
| author_facet | Yuan‐Tao Liu Le‐Le Zhang Zi‐Ying Jiang Xian‐Shu Tian Peng‐Lin Li Pei‐Huang Wu Wen‐Ting Du Bo‐Yu Yuan Chu Xie Guo‐Long Bu Lan‐Yi Zhong Yan‐Lin Yang Ting Li Mu‐Sheng Zeng Cong Sun |
| author_sort | Yuan‐Tao Liu |
| collection | DOAJ |
| description | ABSTRACT Artificial intelligence (AI) is revolutionizing biotechnology by transforming the landscape of therapeutic development. Traditional drug discovery faces persistent challenges, including high attrition rates, billion‐dollar costs, and timelines exceeding a decade. Recent advances in AI—particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models—have introduced data‐driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. However, a comprehensive synthesis of how AI technologies reshape each key modality of drug discovery remains lacking. This review systematically examines AI‐enabled breakthroughs across four major therapeutic platforms: small‐molecule drug design, protein binder discovery, antibody engineering, and nanoparticle‐based delivery systems. It highlights AI's ability to achieve >75% hit validation in virtual screening, design protein binders with sub‐Ångström structural fidelity, enhancing antibody binding affinity to the picomolar range, and optimize nanoparticles to achieve over 85% functionalization efficiency. We further discuss the integration of high‐throughput experimentation, closed‐loop validation, and AI‐guided optimization in expanding the druggable proteome and enabling precision medicine. By consolidating cross‐domain advances, this review provides a roadmap for leveraging machine learning to overcome current biopharmaceutical bottlenecks and accelerate next‐generation therapeutic innovation. |
| format | Article |
| id | doaj-art-9822b5a1707a460cba2b8750f51b50bd |
| institution | Kabale University |
| issn | 2688-2663 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | MedComm |
| spelling | doaj-art-9822b5a1707a460cba2b8750f51b50bd2025-08-20T04:02:50ZengWileyMedComm2688-26632025-08-0168n/an/a10.1002/mco2.70317Applications of Artificial Intelligence in Biotech Drug Discovery and Product DevelopmentYuan‐Tao Liu0Le‐Le Zhang1Zi‐Ying Jiang2Xian‐Shu Tian3Peng‐Lin Li4Pei‐Huang Wu5Wen‐Ting Du6Bo‐Yu Yuan7Chu Xie8Guo‐Long Bu9Lan‐Yi Zhong10Yan‐Lin Yang11Ting Li12Mu‐Sheng Zeng13Cong Sun14State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaState Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou ChinaABSTRACT Artificial intelligence (AI) is revolutionizing biotechnology by transforming the landscape of therapeutic development. Traditional drug discovery faces persistent challenges, including high attrition rates, billion‐dollar costs, and timelines exceeding a decade. Recent advances in AI—particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models—have introduced data‐driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. However, a comprehensive synthesis of how AI technologies reshape each key modality of drug discovery remains lacking. This review systematically examines AI‐enabled breakthroughs across four major therapeutic platforms: small‐molecule drug design, protein binder discovery, antibody engineering, and nanoparticle‐based delivery systems. It highlights AI's ability to achieve >75% hit validation in virtual screening, design protein binders with sub‐Ångström structural fidelity, enhancing antibody binding affinity to the picomolar range, and optimize nanoparticles to achieve over 85% functionalization efficiency. We further discuss the integration of high‐throughput experimentation, closed‐loop validation, and AI‐guided optimization in expanding the druggable proteome and enabling precision medicine. By consolidating cross‐domain advances, this review provides a roadmap for leveraging machine learning to overcome current biopharmaceutical bottlenecks and accelerate next‐generation therapeutic innovation.https://doi.org/10.1002/mco2.70317artificial intelligencedrug discoveryprotein engineering |
| spellingShingle | Yuan‐Tao Liu Le‐Le Zhang Zi‐Ying Jiang Xian‐Shu Tian Peng‐Lin Li Pei‐Huang Wu Wen‐Ting Du Bo‐Yu Yuan Chu Xie Guo‐Long Bu Lan‐Yi Zhong Yan‐Lin Yang Ting Li Mu‐Sheng Zeng Cong Sun Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development MedComm artificial intelligence drug discovery protein engineering |
| title | Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development |
| title_full | Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development |
| title_fullStr | Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development |
| title_full_unstemmed | Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development |
| title_short | Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development |
| title_sort | applications of artificial intelligence in biotech drug discovery and product development |
| topic | artificial intelligence drug discovery protein engineering |
| url | https://doi.org/10.1002/mco2.70317 |
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