Prathipati Philip, Saxena Anil K
Medicinal and Process Chemistry Division, Central Drug Research Institute, Chatter Manzil Palace, Lucknow, India.
J Chem Inf Model. 2006 Jan-Feb;46(1):39-51. doi: 10.1021/ci050120w.
In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.
在当今高通量计算机虚拟筛选的世界中，开发虚拟筛选方法以将小分子作为新化学实体（NCEs）进行合成排序是当前的研究热点。在几种虚拟筛选方法中，基于结构的虚拟筛选被认为是最有效的。然而，在基于结构的虚拟筛选技术中，与根据评分函数对潜在解决方案进行排序相关的问题仍然是主要瓶颈之一。有人提出，评分函数可作为区分结合剂和非结合剂的过滤器，而不是准确预测它们的结合自由能。随后，在这一领域取得了一些改进，包括使用多个而非单个评分函数，以及应用共识或多元统计方法或两者兼用以提高结合剂和非结合剂之间的区分能力。有鉴于此，已将使用LUDI和MOE评分函数推导的二元QSAR模型的区分能力（区分结合剂和非结合剂）与Jacobsson等人在五个数据集上推导的模型进行了比较，这五个数据集分别是雌激素受体α模拟物（ERalpha_mimics）、雌激素受体α毒素（ERalpha_toxins）、基质金属蛋白酶3抑制剂（MMP - 3）、凝血因子Xa抑制剂（fXa）和乙酰胆碱酯酶抑制剂（AChE）。总体分析表明，二元QSAR与Jacobsson等人使用的PLS判别分析、基于规则的和贝叶斯分类方法相当。此外，LUDI和MOE中实现的评分函数可以对广泛的蛋白质 - 配体相互作用进行评分，并且与ICM和Cscore中实现的评分函数相当。因此，使用LUDI和MOE评分函数推导的二元QSAR模型可作为多层虚拟筛选范式中的初步筛选层。
