SAIAME: Semi-Parameter Adaptation Information-Assisted Multi-Objective Evolutionary for Protein-Ligand Docking.
Saved in:
| Title: | SAIAME: Semi-Parameter Adaptation Information-Assisted Multi-Objective Evolutionary for Protein-Ligand Docking. |
|---|---|
| Authors: | Xiao W; School of Electronic and Information, Shanghai Dianji University, Shanghai, China., Shu H; School of Electronic and Information, Shanghai Dianji University, Shanghai, China., Xu C; School of Electronic and Information, Shanghai Dianji University, Shanghai, China., Li W; College of Science, University of Shanghai for Science and Technology, Shanghai, China.; School of Chemical Engineering, University of new South, Sydney, Australia., Ren J; School of Electronic and Information, Shanghai Dianji University, Shanghai, China. |
| Source: | Chemical biology & drug design [Chem Biol Drug Des] 2025 Apr; Vol. 105 (4), pp. e70094. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101262549 Publication Model: Print Cited Medium: Internet ISSN: 1747-0285 (Electronic) Linking ISSN: 17470277 NLM ISO Abbreviation: Chem Biol Drug Des Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Wiley-Blackwell, 2006- |
| MeSH Terms: | Proteins*/chemistry , Proteins*/metabolism , Molecular Docking Simulation*, Ligands ; Algorithms ; Protein Binding ; Binding Sites |
| Abstract: | Molecular docking, which simulates the binding pose of a drug molecule to target proteins and predicts the binding affinity, is an important computational tool in structure-based drug discovery. However, the difficulties of high ligand connectivity and dimensionality reduce the search ability of the conformational sampling. To this end, a semi-parameter adaptation information-assisted multi-objective evolution method named SAIAME is proposed for protein-ligand docking optimization. SAIAME employs a staged and dynamic semi-parameter adaptive updating strategy, in which the crossover rate is updated by a weighted arithmetic average algorithm in the exploration phase, as well as the scaling factor is updated by the Lehmer mean in the exploitation phase. It integrates a gradient enhancement based on infinity norms to smooth the decay of the weights of the learning rate during gradient descent to enhance the handling of outliers. It introduces a population size reduction strategy that combines linear and bilateral symmetric sawtooth functions to enhance its execution efficiency. The experimental results demonstrate that SAIAME not only achieves the accuracies of 87.02% for the best poses and 72.98% for the top-score poses within an RMSD of 2 Å, but also has certain advantages in execution efficiency. (© 2025 John Wiley & Sons Ltd.) |
| References: | Abramson, J., J. Adler, J. Dunger, et al. 2024. “Accurate Structure Prediction of Biomolecular Interactions With AlphaFold 3.” Nature 630, no. 8016: 1–3. https://doi.org/10.1038/s41586‐024‐07487‐w. Allen, W. J., T. E. Balius, S. Mukherjee, et al. 2015. “DOCK 6: Impact of New Features and Current Docking Performance.” Journal of Computational Chemistry 36, no. 15: 1132–1156. https://doi.org/10.1002/jcc.23905. Babu, B. V., and M. M. L. Jehan. 2003. “Differential Evolution for multi‐objective optimization.” In the 2003 Congress on Evolutionary Computation 4: 2696–2703. https://doi.org/10.1109/CEC.2003.1299429. Basak, A., S. Das, and K. C. Tan. 2012. “Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance‐Based Selection.” IEEE Transactions on Evolutionary Computation 17, no. 5: 666–685. https://doi.org/10.1109/TEVC.2012.2231685. Bryant, P., G. Pozzati, and A. Elofsson. 2022. “Improved Prediction of Protein‐Protein Interactions Using AlphaFold2.” Nature Communications 13, no. 1: 1265. https://doi.org/10.1038/s41467‐022‐28865‐w. Friesner, R. A., J. L. Banks, R. B. Murphy, et al. 2004. “Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy.” Journal of Medicinal Chemistry 47, no. 7: 1739–1749. https://doi.org/10.1021/jm0306430. Gupta, M., R. Sharma, and A. Kumar. 2018. “Docking Techniques in Pharmacology: How Much Promising?” Computational Biology and Chemistry 76: 210–217. https://doi.org/10.1016/j.compbiolchem.2018.06.005. Hollingsworth, S. A., and R. O. Dror. 2018. “Molecular Dynamics Simulation for all.” Neuron 99, no. 6: 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011. Jain, A. N. 2003. “Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity‐Based Search Engine.” Journal of Medicinal Chemistry 46, no. 4: 499–511. https://doi.org/10.1021/jm020406h. Ji, J., J. Zhou, Z. Yang, Q. Lin, and C. A. C. Coello. 2022. “Autodock Koto: A Gradient Boosting Differential Evolution for Molecular Docking.” IEEE Transactions on Evolutionary Computation 27, no. 6: 1648–1662. https://doi.org/10.1109/TEVC.2022.3225632. Jones, G., P. Willett, R. C. Glen, A. R. Leach, and R. Taylor. 1997. “Development and Validation of a Genetic Algorithm for Flexible Docking.” Journal of Molecular Biology 267, no. 3: 727–748. https://doi.org/10.1006/jmbi.1996.0897. Kingma, P. D. 2014. “Adam: A Method for Stochastic Optimization. Arxiv Preprint Arxiv:1412.6980”. https://doi.org/10.48550/arXiv.1412.6980. Lyu, J., J. J. Irwin, and B. K. Shoichet. 2023. “Modeling the Expansion of Virtual Screening Libraries.” Nature Chemical Biology 19, no. 6: 712–718. https://doi.org/10.1038/s41589‐022‐01234‐w. Lyu, J., S. Wang, T. E. Balius, et al. 2019. “Ultra‐Large Library Docking for Discovering New Chemotypes.” Nature 566, no. 7743: 224–229. https://doi.org/10.1038/s41586‐019‐0917‐9. Meng, Z., X. Lin, and D. Chen. 2024. “ACD‐DE: An Adaptive Cluster Division Differential Evolution for Mitigating Population Diversity Deficiency.” Information Sciences 679: 121091. https://doi.org/10.1016/j.ins.2024.121091. Mohamed, A. W., A. A. Hadi, A. M. Fattouh, and K. M. Jambi. 2017. “LSHADE With Semi‐Parameter Adaptation Hybrid With CMA‐ES for Solving CEC 2017 Benchmark Problems.” In 2017 Ieee Congress On Evolutionary Computation (Cec): 145–152. https://doi.org/10.1109/CEC.2017.7969307. Morris, G. M., R. Huey, W. Lindstrom, et al. 2009. “AutoDock4 and AutoDockTools4: Automated Docking With Selective Receptor Flexibility.” Journal of Computational Chemistry 30, no. 16: 2785–2791. https://doi.org/10.1002/jcc.21256. Ramsay, R. R., M. R. Popovic‐Nikolic, K. Nikolic, E. Uliassi, and M. L. Bolognesi. 2018. “A Perspective on Multi‐Target Drug Discovery and Design for Complex Diseases.” Clinical and Translational Medicine 7: 1–14. https://doi.org/10.1186/s40169‐017‐0181‐2. Sadybekov, A. V., and V. Katritch. 2023. “Computational Approaches Streamlining Drug Discovery.” Nature 616, no. 7958: 673–685. https://doi.org/10.1038/s41586‐023‐05905‐z. Storn, R. 1995. Differrential Evolution‐a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. Vol. 11. Technical Report, International Computer Science Institute. Su, M., Q. Yang, Y. Du, et al. 2018. “Comparative Assessment of Scoring Functions: The CASF‐2016 Update.” Journal of Chemical Information and Modeling 59, no. 2: 895–913. https://doi.org/10.1021/acs.jcim.8b00545. Sun, D., W. Gao, H. Hu, and S. Zhou. 2022. “Why 90% of Clinical Drug Development Fails and How to Improve It?” Acta Pharmaceutica Sinica B 12, no. 7: 3049–3062. https://doi.org/10.1016/j.apsb.2022.02.002. Sun, G., B. Yang, Z. Yang, and G. Xu. 2020. “An Adaptive Differential Evolution With Combined Strategy for Global Numerical Optimization.” Soft Computing 24: 6277–6296. https://doi.org/10.1007/s00500‐019‐03934‐3. Trivedi, A., K. Sanyal, P. Verma, and D. Srinivasan. 2017. “A Unified Differential Evolution Algorithm for Constrained Optimization Problems.” In 2017 Ieee Congress on Evolutionary Computation (Cec): 1231–1238. https://doi.org/10.1109/CEC.2017.7969446. Trott, O., and A. J. Olson. 2010. “AutoDock Vina: Improving the Speed and Accuracy of Docking With a New Scoring Function, Efficient Optimization, and Multithreading.” Journal of Computational Chemistry 31, no. 2: 455–461. https://doi.org/10.1002/jcc.21334. Wang, R., X. Fang, Y. Lu, and S. Wang. 2004. “The PDBbind Database: Collection of Binding Affinities for Protein—Ligand Complexes With Known Three‐Dimensional Structures.” Journal of Medicinal Chemistry 47, no. 12: 2977–2980. https://doi.org/10.1021/jm030580l. Wu, G., X. Shen, H. Li, H. Chen, A. Lin, and P. N. Suganthan. 2018. “Ensemble of Differential Evolution Variants.” Information Sciences 423: 172–186. https://doi.org/10.1016/j.ins.2017.09.053. Yang, D., K. Zheng, and S. Qian. 2025. “Mitigating interference of microservices with a scoring mechanism in large‐scale clusters.” Journal of Supercomputing 81, no. 1: 1–31. Zhang, J., and A. C. Sanderson. 2009. “JADE: Adaptive Differential Evolution With Optional External Archive.” IEEE Transactions on Evolutionary Computation 13, no. 5: 945–958. https://doi.org/10.1109/TEVC.2009.2014613. |
| Grant Information: | National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: gradient enhancement; multi‐objective evolution; population size reduction; protein‐ligand docking; semi‐parameter adaptation |
| Substance Nomenclature: | 0 (Ligands) 0 (Proteins) |
| Entry Date(s): | Date Created: 20250403 Date Completed: 20250403 Latest Revision: 20250403 |
| Update Code: | 20260130 |
| DOI: | 10.1111/cbdd.70094 |
| PMID: | 40176339 |
| Database: | MEDLINE |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!