吉林大学学报（信息科学版）2026，Vol.44Issue(1)：78-86,9.
面向大规模虚拟筛选的并行增量图贝叶斯优化
Parallel Incremental Graph Bayesian Optimization for Large-Scale Virtual Screening
赵晨阳 1赵海士 1杨博1
作者信息
- 1. 吉林大学计算机科学与技术学院,长春 130012||吉林大学符号计算与知识工程教育部重点实验室,长春 130012
- 折叠
摘要
Abstract
Traditional methods like molecular docking often face high time costs or infeasibility in large-scale virtual screening tasks.To address this problem,a parallel graph Bayesian optimization framework incorporating incremental learning is proposed to efficiently handle such tasks.The method utilizes a deep graph Bayesian optimization framework for screening and employs parallelization to enable flexible deployment across multiple computational nodes on various servers,significantly improving computational efficiency.To tackle the issue of long surrogate model training times,an incremental learning strategy is introduced,along with an exponential moving average mechanism and a replay mechanism to mitigate catastrophic forgetting in incremental learning.Experimental results demonstrate that the framework can identify over 96％of the optimal molecules by docking only 6％of the molecular library.When deployed on four computational nodes,the parallel framework reduces time costs by 71％compared to the serial framework.With the incremental learning strategy,the total runtime is further reduced by 13.8％,while still identifying 93.7％of the optimal molecules.The proposed method significantly reduces the time cost of virtual screening while maintaining high screening performance.关键词
并行/虚拟筛选/贝叶斯优化/增量学习Key words
parallel/virtual screening/Bayesian optimization/incremental learning分类
信息技术与安全科学引用本文复制引用
赵晨阳,赵海士,杨博..面向大规模虚拟筛选的并行增量图贝叶斯优化[J].吉林大学学报（信息科学版）,2026,44(1):78-86,9.
