Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand–receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand–receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a “voting” approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

计算机辅助策略有助于降低成本并提高药物发现的成功率。在这些策略中，基于药效团（代表目标活性位点的电子和空间特征的集合）的方法可以有效地在大型化合物库上实施。然而，传统的基于药效基团的方法需要了解活性化合物或配体-受体结构，并且只有少数方法能够解释靶标的灵活性。在这里，我们开发了一种基于药效基团的虚拟筛选方案 Flexi-pharma，克服了这些限制。该协议使用分子动力学 (MD) 模拟来探索受体的灵活性，并对一组 MD 构象进行基于药效团的虚拟筛选，而无需事先了解已知配体或配体-受体结构来构建药效团。来自不同受体构象的结果使用“投票”方法进行组合，其中对与每个 MD 构象中至少一个药效团相匹配的每个分子进行投票。与将药效团集合简化为一些代表性模型并根据匹配模型或分子构象异构体评分的其他方法相反，Flexi-pharma 方法通过对受体构象进行评分来直接考虑受体灵活性。我们在 20 个系统上测试了该方法，发现其中 19 个系统的数据集得到了丰富。Flexi-pharma 的计算效率很高，可以在单个 CPU 内核上在几分钟内筛选数千种化合物。此外，通过投票对分子进行排序是一种通用策略，可以应用于任何药效团过滤程序。