Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery
Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery
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DOI：
10.1021/acsmedchemlett.9b00499
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发表时间：
2020-01-09
影响因子：
4.2
通讯作者：
Haywood, Alexe
中科院分区：
文献类型：
--
作者：
Bodnarchuk, Michael S.;Packer, Martin J.;Haywood, Alexe
以水域为目标以获得效力和选择性收益背后的概念已经被很好地记录和探索，尽管最大限度地提高这种潜在收益可能被证明是具有挑战性的。如果有多个相互作用的水域，其中一个水域的扰动可能会影响其余水域的自由能景观，这个问题就会加剧。事先知道正确的修改是具有挑战性的，而计算方法非常适合于帮助回答以下关键问题：最好尝试哪种替代方法。在这里，我们使用大正则蒙特卡罗和最近的大正则炼金术微扰方法来理解和预测当涉及多个水分子时配体介导的水置换的影响，以及了解利用水网络如何帮助控制选择性。
The concepts behind targeting waters for potency and selectivity gains have been well documented and explored, although maximizing such potential gains can prove to be challenging. This problem is exacerbated in cases where there are multiple interacting waters, wherein perturbation of one water can affect the free energy landscape of the remaining waters. Knowing the right modification a priori is challenging, and computational approaches are ideally suited to help answer the key question of which substitution is best to try. Here, we use Grand Canonical Monte Carlo and the recent Grand Canonical Alchemical Perturbation methods to both understand and predict the effect of ligand-mediated water displacement when more than one water molecule is involved, as well as to understand how exploiting water networks can help govern selectivity.