Universidade de São Paulo, Instituto de Química de São Carlos, Grupo de Estudos em Química Medicinal de Produtos Naturais - NEQUIME-PN, 13560-970, São Carlos, SP, Brazil +55 16 3373 9986 ; +55 16 3373 9985 ;
Expert Opin Drug Discov. 2010 Apr;5(4):333-46. doi: 10.1517/17460441003652959.
Bimolecular recognition is the basis for almost all processes in biological systems. The geometrical and chemical complementarity of small molecule ligands and their macromolecular biological targets, matching paired interacting parts, can result in binding that will eventually yield a biological response.
The topics covered include the integration of molecular interaction fields and chemometrics, via the GRID/CPCA (consensus principal component analysis) method that is actively contributing to the optimization of potency and selectivity of ligands towards a chosen target. Key applications that hallmark the usefulness of the method are critically presented. By comparison of the GRID/CPCA and GRID/PCA, the breakthroughs and challenges are highlighted.
Molecular recognition studies support the development of pharmacophore-based descriptors, which provide the means to identify new ligand templates ('scaffold-hopping'). The GRID/CPCA approach can simultaneously reveal common trends in more than one block of data for more than two target proteins, with several three-dimensional structures per protein. It offers the benefit of improving the weighting between different interaction energy probes within the GRID parameterization. An important consequence is that hydrophobic interactions can be assessed for selectivity between proteins.
Molecular-field-based methods along with CPCA analysis are extremely powerful to understand bimolecular interactions. Because drug discovery and development is a costly, time consuming and high-risk activity and GRID/CPCA is at the forefront of the computer-aided design, it should be used as early as possible for discovering new drugs.
双分子识别是几乎所有生物系统过程的基础。小分子配体与其生物大分子靶标的几何和化学互补性，匹配配对的相互作用部分，可以导致结合，最终产生生物响应。
涵盖的主题包括通过 GRID/CPCA（共识主成分分析）方法整合分子相互作用场和化学计量学，该方法积极有助于优化配体对选定靶标的效力和选择性。批判性地呈现了标志着该方法有用性的关键应用。通过比较 GRID/CPCA 和 GRID/PCA，突出了突破和挑战。
分子识别研究支持基于药效团的描述符的开发，这为识别新的配体模板（“支架跳跃”）提供了手段。GRID/CPCA 方法可以同时揭示两个以上目标蛋白质中超过一个数据块的共同趋势，每个蛋白质有几个三维结构。它提供了改善 GRID 参数化中不同相互作用能量探针之间加权的好处。一个重要的结果是，可以评估蛋白质之间的选择性的疏水性相互作用。
基于分子场的方法与 CPCA 分析相结合，对于理解双分子相互作用非常有效。由于药物发现和开发是一项昂贵、耗时且高风险的活动，并且 GRID/CPCA 处于计算机辅助设计的前沿，因此应该尽早用于发现新药。
