ReMODE: a deep learning-based web server for target-specific drug design
Abstract Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, an...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-022-00665-w |
| _version_ | 1828115304515895296 |
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| author | Mingyang Wang Jike Wang Gaoqi Weng Yu Kang Peichen Pan Dan Li Yafeng Deng Honglin Li Chang-Yu Hsieh Tingjun Hou |
| author_facet | Mingyang Wang Jike Wang Gaoqi Weng Yu Kang Peichen Pan Dan Li Yafeng Deng Honglin Li Chang-Yu Hsieh Tingjun Hou |
| author_sort | Mingyang Wang |
| collection | DOAJ |
| description | Abstract Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ . Graphical Abstract |
| first_indexed | 2024-04-11T12:38:17Z |
| format | Article |
| id | doaj.art-db1f876706704fe9b5f75e3374bfbc46 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-04-11T12:38:17Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-db1f876706704fe9b5f75e3374bfbc462022-12-22T04:23:34ZengBMCJournal of Cheminformatics1758-29462022-12-0114111110.1186/s13321-022-00665-wReMODE: a deep learning-based web server for target-specific drug designMingyang Wang0Jike Wang1Gaoqi Weng2Yu Kang3Peichen Pan4Dan Li5Yafeng Deng6Honglin Li7Chang-Yu Hsieh8Tingjun Hou9Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityCarbonSilicon AI Technology Co., LtdShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & TechnologyInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang UniversityAbstract Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ . Graphical Abstracthttps://doi.org/10.1186/s13321-022-00665-wDeep learningDe novo drug designMolecular generationAdversarial autoencodersTransfer learningArtificial intelligence |
| spellingShingle | Mingyang Wang Jike Wang Gaoqi Weng Yu Kang Peichen Pan Dan Li Yafeng Deng Honglin Li Chang-Yu Hsieh Tingjun Hou ReMODE: a deep learning-based web server for target-specific drug design Journal of Cheminformatics Deep learning De novo drug design Molecular generation Adversarial autoencoders Transfer learning Artificial intelligence |
| title | ReMODE: a deep learning-based web server for target-specific drug design |
| title_full | ReMODE: a deep learning-based web server for target-specific drug design |
| title_fullStr | ReMODE: a deep learning-based web server for target-specific drug design |
| title_full_unstemmed | ReMODE: a deep learning-based web server for target-specific drug design |
| title_short | ReMODE: a deep learning-based web server for target-specific drug design |
| title_sort | remode a deep learning based web server for target specific drug design |
| topic | Deep learning De novo drug design Molecular generation Adversarial autoencoders Transfer learning Artificial intelligence |
| url | https://doi.org/10.1186/s13321-022-00665-w |
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