Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability
Computerized molecular docking methodologies are pivotal in in-silico screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking met...
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| Format: | Article |
| Language: | English |
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The Biophysical Society of Japan
2024-10-01
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| Series: | Biophysics and Physicobiology |
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| Online Access: | https://doi.org/10.2142/biophysico.bppb-v21.0021 |
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| _version_ | 1841553378758361088 |
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| author | Akihiro Masuda Daichi Sadato Mitsuo Iwadate |
| author_facet | Akihiro Masuda Daichi Sadato Mitsuo Iwadate |
| author_sort | Akihiro Masuda |
| collection | DOAJ |
| description | Computerized molecular docking methodologies are pivotal in in-silico screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking methodologies, there remains a gap in optimizing the predictive capabilities of docking simulation software. Accordingly, using the docking scores output by ChooseLD, we evaluated its performance in predicting the bioactivity of G-protein coupled receptor (GPCR) and kinase bioactivity, specifically focusing on Ki and IC50 values. We evaluated the accuracy of our algorithm through a comparative analysis using force-field-based predictions from AutoDock Vina. Our findings suggested that the modified ChooseLD could accurately predict the bioactivity, especially in scenarios with a substantial number of known ligands. These findings highlight the importance of selecting algorithms based on the characteristics of the prediction targets. Furthermore, addressing partial model fitting with database knowledge was demonstrated to be effective in overcoming this challenge. Overall, these findings contribute to the refinement and optimization of methodologies in computer-aided drug design, ultimately advancing the efficiency and reliability of in-silico screening processes. |
| format | Article |
| id | doaj-art-5d4729c820204538b3bd2127ae20d0ba |
| institution | Kabale University |
| issn | 2189-4779 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Biophysical Society of Japan |
| record_format | Article |
| series | Biophysics and Physicobiology |
| spelling | doaj-art-5d4729c820204538b3bd2127ae20d0ba2025-01-09T09:57:06ZengThe Biophysical Society of JapanBiophysics and Physicobiology2189-47792024-10-012110.2142/biophysico.bppb-v21.0021Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictabilityAkihiro Masuda0Daichi Sadato1Mitsuo Iwadate2Department of Biological Sciences, Graduate School of Science and Engineering, Chuo University, Bunkyo-ku, Tokyo 112-8551, JapanClinical Research and Trials Center, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Bunkyo-ku, Tokyo 113-0021, JapanDepartment of Biological Sciences, Faculty of Science and Engineering, Chuo University, Bunkyo-ku, Tokyo 112-8551, JapanComputerized molecular docking methodologies are pivotal in in-silico screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking methodologies, there remains a gap in optimizing the predictive capabilities of docking simulation software. Accordingly, using the docking scores output by ChooseLD, we evaluated its performance in predicting the bioactivity of G-protein coupled receptor (GPCR) and kinase bioactivity, specifically focusing on Ki and IC50 values. We evaluated the accuracy of our algorithm through a comparative analysis using force-field-based predictions from AutoDock Vina. Our findings suggested that the modified ChooseLD could accurately predict the bioactivity, especially in scenarios with a substantial number of known ligands. These findings highlight the importance of selecting algorithms based on the characteristics of the prediction targets. Furthermore, addressing partial model fitting with database knowledge was demonstrated to be effective in overcoming this challenge. Overall, these findings contribute to the refinement and optimization of methodologies in computer-aided drug design, ultimately advancing the efficiency and reliability of in-silico screening processes.https://doi.org/10.2142/biophysico.bppb-v21.0021autodock vinamolecular dockinggpcrkinase |
| spellingShingle | Akihiro Masuda Daichi Sadato Mitsuo Iwadate Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability Biophysics and Physicobiology autodock vina molecular docking gpcr kinase |
| title | Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability |
| title_full | Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability |
| title_fullStr | Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability |
| title_full_unstemmed | Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability |
| title_short | Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand–protein docking predictability |
| title_sort | data driven score tuning for chooseld a structure based drug design algorithm with empirical scoring and evaluation of ligand protein docking predictability |
| topic | autodock vina molecular docking gpcr kinase |
| url | https://doi.org/10.2142/biophysico.bppb-v21.0021 |
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