High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates inference costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration. We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization. DSP represents an attractive extension of model-guided optimization that can limit overhead costs in optimization settings where these costs are non-negligible relative to objective costs, such as docking.

中文翻译：

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具有主动设计空间修剪的自聚焦虚拟筛选

高通量虚拟筛选是发现小分子不可或缺的技术。在分子库非常大的情况下，详尽的虚拟屏幕的成本可能会令人望而却步。与随机选择相比，模型引导优化已被用于通过显着提高样本效率来降低这些成本。然而，这些技术通过代理模型训练和推理步骤为工作流程引入了新的成本。在这项研究中，我们提出了对模型引导优化框架的扩展，该框架使用我们称为设计空间修剪 (DSP) 的技术来降低推理成本，该技术不可逆转地将表现不佳的候选者排除在考虑之外。我们研究了 DSP 在各种优化任务中的应用，并观察到开销成本显着降低，同时表现出与基线优化相似的性能。DSP 代表了模型引导优化的一个有吸引力的扩展，它可以限制优化设置中的开销成本，这些成本相对于目标成本是不可忽略的，例如对接。