Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study

数量结构-活动关系 试验装置 量子化学 分子描述符 适用范围 化学 人工智能 计算化学 计算机科学 分子 立体化学 有机化学
作者
Ashish Gupta,Virender Kumar,Polamarasetty Aparoy
出处
期刊:Current Topics in Medicinal Chemistry [Bentham Science Publishers]
卷期号:18 (13): 1075-1090 被引量:12
标识
DOI:10.2174/1568026618666180719164149
摘要

Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
我我我完成签到,获得积分10
1秒前
橘络发布了新的文献求助10
1秒前
称心采枫完成签到 ,获得积分0
2秒前
SYLH应助飞快的寒香采纳,获得10
2秒前
舟舟完成签到 ,获得积分10
3秒前
学茶小白发布了新的文献求助10
3秒前
十一完成签到,获得积分10
3秒前
Yanis完成签到,获得积分10
4秒前
猪猪侠完成签到,获得积分10
4秒前
油炸丸子完成签到,获得积分10
4秒前
yoyo发布了新的文献求助10
4秒前
hu完成签到,获得积分10
5秒前
TianFuAI发布了新的文献求助10
5秒前
XPN完成签到,获得积分10
6秒前
lullaby完成签到,获得积分10
6秒前
崇凛关注了科研通微信公众号
6秒前
张星星完成签到 ,获得积分10
6秒前
6秒前
7秒前
油炸丸子发布了新的文献求助10
7秒前
黄科研完成签到,获得积分10
7秒前
小丑完成签到 ,获得积分10
7秒前
简单的梦菡完成签到,获得积分10
8秒前
謓言发布了新的文献求助10
9秒前
鹅帮逮完成签到,获得积分10
9秒前
10秒前
学茶小白完成签到,获得积分10
10秒前
10秒前
u点小糕冷发布了新的文献求助10
10秒前
10秒前
我要发nature完成签到,获得积分10
10秒前
wo发布了新的文献求助10
11秒前
bkagyin应助细心的盼易采纳,获得10
11秒前
俭朴的宛完成签到 ,获得积分10
12秒前
傻妞发布了新的文献求助10
13秒前
小甜完成签到,获得积分10
13秒前
WYang完成签到,获得积分10
13秒前
知之然完成签到,获得积分10
13秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Social Research Methods (4th Edition) by Maggie Walter (2019) 2390
A new approach to the extrapolation of accelerated life test data 1000
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4009325
求助须知:如何正确求助?哪些是违规求助? 3549162
关于积分的说明 11301105
捐赠科研通 3283572
什么是DOI,文献DOI怎么找? 1810370
邀请新用户注册赠送积分活动 886205
科研通“疑难数据库(出版商)”最低求助积分说明 811301