Seccion Fisico Quimica y Matematicas, Departamento de Quimica, Universidad Tecnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador.
Molecular Simulation and Drug Design Group, Centro de Bioactivos Quimicos (CBQ), Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Cuba.
Curr Neuropharmacol. 2017 Nov 14;15(8):1107-1116. doi: 10.2174/1570159X15666170109143757.
Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease.
In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands.
The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors.
Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments.
虚拟方法已成为药物发现管道的重要组成部分。具体来说，基于结构的药物设计方法利用分子靶标的 3D 结构，通过分子对接发现新的药物候选物。最近，已提出腺苷 A2A 受体和单胺氧化酶 B 酶的双重靶标配体作为治疗帕金森病的有效疗法。
在本文中，我们提出了一种经过广泛验证的基于结构的方法，用于发现双重腺苷 A2A 受体/单胺氧化酶 B 配体。该方法涉及针对两种受体的分子对接研究，以及评估不同评分函数融合策略，以最大限度地提高已知双重配体的初始虚拟筛选富集度。
开发的方法提供了已知配体的高富集值，优于单个评分函数。同时，可以将获得的集合转化为一系列步骤，以最大限度地提高双重靶标腺苷 A2A 受体拮抗剂和单胺氧化酶 B 抑制剂的富集度。
必须结合两个靶标相对应的对接分数信息，以实现高双重配体的富集。事实证明，将不同评分函数的排名相结合是提高虚拟筛选实验中相对于单个评分函数的富集度的一种有价值的策略。
