In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

中文翻译：

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用于虚拟药物筛选优化的拥塞游戏调度。

在虚拟药物筛选中，命中的化学多样性以及其预测的活性是一个重要因素。此外，中期结果对于指导进一步研究很有意义，并且它们的多样性也是可取的。在本文中，我们考虑了一个在短时间内获得各种虚拟筛选结果的问题。为此，我们提出了一种用于高性能计算系统中虚拟药物筛选的任务调度数学模型，作为计算节点之间的拥塞博弈，以找到平衡方案，以最佳地平衡临时打击的次数与其化学多样性。该模型考虑具有工作量不确定性，处理时间不确定性以及对输入数据集结构的了解有限的异构环境。我们进行计算实验并考虑有机分子数据库GDB-9评估开发方法的性能。所使用的分子集足够丰富，足以证明所提出解决方案的可行性和实用性。我们将该算法与实践中使用的两种已知启发式算法进行了比较，并观察到基于游戏的调度在命中率和化学多样性上均优于早期步骤。基于这些结果，我们使用社会效用度量标准来评估均衡解决方案的效率，并表明它们达到了最大价值。我们将该算法与实践中使用的两种已知启发式算法进行了比较，并观察到基于游戏的调度在命中率和化学多样性上均优于早期步骤。基于这些结果，我们使用社会效用度量标准来评估均衡解决方案的效率，并表明它们达到了最大价值。我们将该算法与实践中使用的两种已知启发式算法进行了比较，并观察到基于游戏的调度在命中率和化学多样性上均优于早期步骤。基于这些结果，我们使用社会效用度量标准来评估均衡解决方案的效率，并表明它们达到了最大价值。