Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Machine Learning Based Region Search of Successful Parameters
2.1.1. Parameter Sampling By Bo
2.1.2. Parameter Sampling by Us
2.1.3. Combination of BO and US
2.2. F1-Motor and CG-MD Simulation
2.3. Simulation Dynamics and Sampled Parameter Space
2.4. Definition of Success Rate for the Rotation of Subunit in F1-Motor
3. Results
3.1. Sampling Performances for F1-Motor Simulations Using Newtonian Dynamics
3.2. Sampling Performance of F1-Motor Simulations Using Langevin Dynamics
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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SP | BOUS | US | BO | RS | |
---|---|---|---|---|---|
1.0 | 5 (1.98%) | 31 (12.3%) | 115 (45.6%) | 67 (26.6%) | 237 (94.0%) |
0.9 | 8 (3.17%) | 36 (14.3%) | 90 (35.7%) | 100 (39.7%) | 241 (95.6%) |
0.8 | 15 (5.95%) | 45 (17.9%) | 56 (22.2%) | 114 (45.2%) | 242 (96.0%) |
0.7 | 22 (8.73%) | 60 (23.8%) | 65 (25.8%) | 129 (51.2%) | 238 (94.4%) |
0.6 | 29 (11.5%) | 80 (31.7%) | 82 (32.5%) | 146 (57.9%) | 241 (95.6%) |
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Kanada, R.; Tokuhisa, A.; Tsuda, K.; Okuno, Y.; Terayama, K. Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning. Biomolecules 2020, 10, 482. https://doi.org/10.3390/biom10030482
Kanada R, Tokuhisa A, Tsuda K, Okuno Y, Terayama K. Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning. Biomolecules. 2020; 10(3):482. https://doi.org/10.3390/biom10030482
Chicago/Turabian StyleKanada, Ryo, Atsushi Tokuhisa, Koji Tsuda, Yasushi Okuno, and Kei Terayama. 2020. "Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning" Biomolecules 10, no. 3: 482. https://doi.org/10.3390/biom10030482