利用有限的实验活性数据适配Boltz-2可提高虚拟筛选中的早期富集

中文摘要 AI 辅助

虚拟筛选旨在有限的实验预算内，从大型化学库中优先排序出活性化合物。当将Boltz-2应用于虚拟筛选时，一个关键挑战是如何利用来自目标测定的有限实验数据来改进活性化合物的优先排序。我们研究了使用少量二元活性标签对Boltz-2亲和力头进行微调，是否能够提高命中发现中活性化合物的早期富集。我们在八个MF-PCBA靶标上的回顾性评估中，比较了使用40-300个标签进行微调的效果。在300个活性测量值下，微调使八个靶标中前1%的活性化合物数量几何平均增加了1.77倍，并且相对于未微调的对照组，平均精度（AP）提高了2.14倍。我们还研究了仅对候选子集进行重新评分，即仅使用微调后的头对排名靠前的Boltz-2候选进行重新排序，是否能够保持命中恢复的改进。将重新评分限制在评估集的约10%时，其命中恢复与完全重新评分相当。这些发现表明，使用有限的活性标签进行亲和力头微调可以改善Boltz-2的早期富集，并且在对受限候选集进行重新评分时，这种益处可以得以保留。

英文摘要

Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether fine-tuning the Boltz-2 affinity heads with a small number of binary activity labels could improve early enrichment of active compounds in hit discovery. We compared fine-tuning with 40-300 labels in a retrospective evaluation on eight MF-PCBA targets. With 300 activity measurements, fine-tuning increased the number of actives in the top 1% by a geometric mean of 1.77-fold across the eight targets and improved average precision (AP) by 2.14-fold relative to the control without fine-tuning. We also investigated whether rescoring a subset of candidates could retain the improvement in hit recovery by reranking only the top-ranked Boltz-2 candidates with the fine-tuned head. Restricting rescoring to approximately 10% of the evaluation set retained hit recovery comparable to full rescoring. These findings show that affinity-head fine-tuning with limited activity labels improves early enrichment with Boltz-2 and that this benefit can be retained when rescoring a restricted set of candidates.

发表机构

• Institute of Science Tokyo（东京科学研究所）

机构由 AI 辅助整理，请以论文原文为准。