Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel.
Mol Inform. 2022 Nov;41(11):e2200034. doi: 10.1002/minf.202200034. Epub 2022 Jul 26.
Docking-based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand-based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub-optimal. With this in mind, in this work we present a method for the development of target-specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment-like metric. Since EOA requires target-specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD-E database, and used them to re-derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target-specific, modified functions. We then used the original ChemPLP function in small-scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re-docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF , two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA-based optimization for the derivation of target-specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.
基于对接的虚拟筛选（VS）是许多药物发现项目的常见起点。虽然配体方法有时可能会提供更好的结果，但对接的优势在于它能够提供可靠的配体结合模式和近似的结合自由能，这两个因素对于命中选择和优化很重要。大多数对接程序的开发尽可能通用，因此它们在特定目标上的性能可能不理想。考虑到这一点，在这项工作中，我们提出了一种使用我们最近报道的富集优化算法（EOA）开发针对特定目标的评分函数的方法。EOA 通过优化类似于富集的度量标准，以多元线性回归（MLR）方程的形式导出 QSAR 模型。由于 EOA 需要针对特定目标的活性和非活性（或诱饵）化合物，我们从 DUD-E 数据库中为六个目标检索了此类数据，并使用它们重新推导构成 GOLD 的 ChemPLP 评分函数的相关组件的权重，从而产生针对特定目标的修改函数。然后，我们在六个目标的小规模 VS 实验中使用原始 ChemPLP 函数，随后使用修改后的函数对得到的构象进行重新评分。此外，我们还使用修改后的函数对化合物进行重新对接。我们发现，在许多情况下（尽管不是所有情况），无论是重新评分原始 ChemPLP 构象还是使用修改后的函数重复整个对接过程，都可以在 AUC 和 EF 方面获得更好的结果，这两个指标常用于评估 VS 的性能。虽然显然需要对其他数据集和对接工具进行研究，但我们认为迄今为止获得的结果表明，在虚拟筛选中使用基于 EOA 的优化来推导针对特定目标的函数具有潜在的好处。为此，我们讨论了该方法的缺点以及如何改进它。
