التفاصيل البيبلوغرافية
| العنوان: |
Reinforcement learning to boost molecular docking upon protein conformational ensemble. |
| المؤلفون: |
Chong, Bin, Yang, Yingguang, Wang, Zi-Le, Xing, Han, Liu, Zhirong |
| المصدر: |
Physical Chemistry Chemical Physics (PCCP); 3/21/2021, Vol. 23 Issue 11, p6800-6806, 7p |
| مستخلص: |
Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs. [ABSTRACT FROM AUTHOR] |
|
Copyright of Physical Chemistry Chemical Physics (PCCP) is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
