Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts. However, their large size presents a challenge for virtual screening, where the goal is to identify the top compounds in a library according to a computational objective (e.g., optimizing docking score) subject to computational constraints under a limited computational budget. For current library sizes -- numbering in the tens of billions of compounds -- and scoring functions of interest, a routine virtual screening campaign may be limited to scoring fewer than 0.1% of the available compounds, leaving potentially many high scoring compounds undiscovered. Furthermore, as constraints (and sometimes objectives) change during the course of a virtual screening campaign, existing virtual screening algorithms typically offer little room for amortization. We propose the approximate-but-exhaustive search protocol for CSLs, or APEX. APEX utilizes a neural network surrogate that exploits the structure of CSLs in the prediction of objectives and constraints to make full enumeration on a consumer GPU possible in under a minute, allowing for exact retrieval of approximate top-$k$ sets. To demonstrate APEX's capabilities, we develop a benchmark CSL comprised of more than 10 million compounds, all of which have been annotated with their docking scores on five medically relevant targets along with physicohemical properties measured with RDKit such that, for any objective and set of constraints, the ground truth top-$k$ compounds can be identified and compared against the retrievals from any virtual screening algorithm. We show APEX's consistently strong performance both in retrieval accuracy and runtime compared to alternative methods.

翻译：按需合成的组合合成库（如Enamine REAL）极大地推动了药物发现进程。然而，其庞大的规模给虚拟筛选带来了挑战——虚拟筛选的目标是在有限的计算资源下，根据计算目标（例如优化对接分数）在计算约束条件下从库中识别出最优化合物。针对当前库规模（高达数百亿化合物）及相关评分函数，常规虚拟筛选可能仅能对不足0.1%的可用化合物进行评分，导致大量潜在高分化合物未被发现。此外，当虚拟筛选过程中约束条件（有时包括目标函数）发生变化时，现有虚拟筛选算法通常难以实现计算成本的摊销。我们提出面向组合合成库的近似但穷举搜索协议APEX。APEX利用神经网络代理模型，通过挖掘组合合成库在预测目标函数与约束条件时的结构特征，使得在消费级GPU上实现全库枚举可在1分钟内完成，从而能够精确获取近似的top-$k$化合物集合。为验证APEX的性能，我们构建了一个包含超过1000万化合物的基准组合合成库，其中所有化合物均标注了针对五个医学相关靶点的对接分数，并通过RDKit计算了物理化学性质。该基准库使得针对任意目标函数与约束条件组合，均可确定真实top-$k$化合物集合，并与任何虚拟筛选算法的检索结果进行对比。实验表明，与其他方法相比，APEX在检索精度与运行时间方面均表现出持续优异的性能。