De novo generation of dual-target compounds using artificial intelligence
Summary: Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compound...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224027536 |
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| author | Kasumi Yasuda Francois Berenger Kazuma Amaike Ayaka Ueda Tomoya Nakagomi Genki Hamasaki Chen Li Noriko Yuyama Otani Kazuma Kaitoh Koji Tsuda Kenichiro Itami Yoshihiro Yamanishi |
| author_facet | Kasumi Yasuda Francois Berenger Kazuma Amaike Ayaka Ueda Tomoya Nakagomi Genki Hamasaki Chen Li Noriko Yuyama Otani Kazuma Kaitoh Koji Tsuda Kenichiro Itami Yoshihiro Yamanishi |
| author_sort | Kasumi Yasuda |
| collection | DOAJ |
| description | Summary: Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks. Using the proposed methods, we designed the chemical structures of compounds that would interact with two therapeutic targets of bronchial asthma, i.e., adenosine A2a receptor (ADORA2A) and phosphodiesterase 4D (PDE4D). We then synthesized 10 compounds and evaluated their bioactivities via the binding assays of 39 target human proteins, including ADORA2A and PDE4D. Three of the 10 synthesized compounds successfully interacted with ADORA2A and PDE4D with high specificity. |
| format | Article |
| id | doaj-art-ba6eec3db7f04145b4d70de86dec0301 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-ba6eec3db7f04145b4d70de86dec03012025-08-20T01:58:33ZengElsevieriScience2589-00422025-01-0128111152610.1016/j.isci.2024.111526De novo generation of dual-target compounds using artificial intelligenceKasumi Yasuda0Francois Berenger1Kazuma Amaike2Ayaka Ueda3Tomoya Nakagomi4Genki Hamasaki5Chen Li6Noriko Yuyama Otani7Kazuma Kaitoh8Koji Tsuda9Kenichiro Itami10Yoshihiro Yamanishi11Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, JapanGraduate School of Science, Nagoya University, Chikusa, Nagoya, Aichi 464-8602, JapanGraduate School of Science, Nagoya University, Chikusa, Nagoya, Aichi 464-8602, JapanGraduate School of Science, Nagoya University, Chikusa, Nagoya, Aichi 464-8602, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, JapanGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, JapanGraduate School of Science, Nagoya University, Chikusa, Nagoya, Aichi 464-8602, Japan; Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya, Aichi 464-8602, JapanDepartment of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan; Corresponding authorSummary: Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks. Using the proposed methods, we designed the chemical structures of compounds that would interact with two therapeutic targets of bronchial asthma, i.e., adenosine A2a receptor (ADORA2A) and phosphodiesterase 4D (PDE4D). We then synthesized 10 compounds and evaluated their bioactivities via the binding assays of 39 target human proteins, including ADORA2A and PDE4D. Three of the 10 synthesized compounds successfully interacted with ADORA2A and PDE4D with high specificity.http://www.sciencedirect.com/science/article/pii/S2589004224027536Natural sciencesBiological sciencesBioinformaticsPharmacoinformatics |
| spellingShingle | Kasumi Yasuda Francois Berenger Kazuma Amaike Ayaka Ueda Tomoya Nakagomi Genki Hamasaki Chen Li Noriko Yuyama Otani Kazuma Kaitoh Koji Tsuda Kenichiro Itami Yoshihiro Yamanishi De novo generation of dual-target compounds using artificial intelligence iScience Natural sciences Biological sciences Bioinformatics Pharmacoinformatics |
| title | De novo generation of dual-target compounds using artificial intelligence |
| title_full | De novo generation of dual-target compounds using artificial intelligence |
| title_fullStr | De novo generation of dual-target compounds using artificial intelligence |
| title_full_unstemmed | De novo generation of dual-target compounds using artificial intelligence |
| title_short | De novo generation of dual-target compounds using artificial intelligence |
| title_sort | de novo generation of dual target compounds using artificial intelligence |
| topic | Natural sciences Biological sciences Bioinformatics Pharmacoinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224027536 |
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