Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

中文翻译：

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优化使用探针渗透性识别膜泛分析干扰化合物的计算机程序

膜泛测定干扰化合物 (PAINS) 是一类与脂质双层非特异性相互作用并改变其物理化学性质的分子。及早识别这些化合物可以避免在药物发现活动中追逐错误的线索和不必要的时间和资源浪费。在这项工作中，我们优化了计算机模拟基于伞式采样 (US)/分子动力学 (MD) 模拟的协议，用于区分具有不同膜 PAINS 行为的化合物。我们表明该方法对膜厚度波动非常敏感，通过将 US 参考位置更改为最接近相互作用的单层的磷酸原子来减轻这种波动。在我们的 US 方案中，通过减少雨伞的数量并调整它们的强度和位置，计算效率得到进一步提高。用于计算膜渗透系数的不均匀溶解度扩散模型 (ISDM) 证实白藜芦醇和姜黄素具有明显的膜疼痛特征，并表明在之前的工作中对 nothofagin 的错误分类。总的来说，我们在这里展示了一个有前途的计算机可作为未来参考方法识别膜疼痛的协议。