亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation

药效团 虚拟筛选 G蛋白偶联受体 计算生物学 药物发现 计算机科学 化学 立体化学 生物 受体 生物化学
作者
Gregory L. Szwabowski,Jon C. Cole,Daniel L. Baker,Abby L. Parrill
出处
期刊:Journal of Molecular Graphics & Modelling [Elsevier]
卷期号:121: 108429-108429 被引量:1
标识
DOI:10.1016/j.jmgm.2023.108429
摘要

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
杪夏二八完成签到 ,获得积分10
2秒前
YY发布了新的文献求助10
7秒前
cx应助科研通管家采纳,获得10
7秒前
天天快乐应助科研通管家采纳,获得10
7秒前
YY完成签到,获得积分10
24秒前
小小虾完成签到 ,获得积分10
30秒前
科研通AI6.1应助追寻梦之采纳,获得10
33秒前
英姑应助hhmiao_o采纳,获得10
41秒前
jyy应助slayersqin采纳,获得10
50秒前
Hello应助感谢采纳,获得100
50秒前
51秒前
了晨完成签到 ,获得积分10
56秒前
hhmiao_o发布了新的文献求助10
57秒前
黑摄会阿Fay完成签到,获得积分10
1分钟前
cc发布了新的文献求助10
1分钟前
1分钟前
追寻梦之发布了新的文献求助10
1分钟前
1分钟前
2分钟前
alee完成签到,获得积分10
2分钟前
隐形曼青应助侯小菊采纳,获得10
2分钟前
烟花应助侯小菊采纳,获得10
2分钟前
Able完成签到,获得积分10
3分钟前
丘比特应助追寻梦之采纳,获得10
3分钟前
4分钟前
追寻梦之发布了新的文献求助10
4分钟前
务实曼冬完成签到 ,获得积分10
4分钟前
gerherg完成签到 ,获得积分10
4分钟前
傻傻的从梦完成签到 ,获得积分10
5分钟前
举个栗子8完成签到 ,获得积分10
5分钟前
6分钟前
6分钟前
6分钟前
Hayat应助科研通管家采纳,获得30
6分钟前
ZHEN发布了新的文献求助10
6分钟前
ZHEN完成签到,获得积分10
6分钟前
千里草完成签到,获得积分10
6分钟前
小枣完成签到 ,获得积分10
6分钟前
LIAO完成签到,获得积分10
6分钟前
6分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 2000
Research for Social Workers 1000
Mastering New Drug Applications: A Step-by-Step Guide (Mastering the FDA Approval Process Book 1) 800
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Streptostylie bei Dinosauriern nebst Bemerkungen über die 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5907926
求助须知:如何正确求助?哪些是违规求助? 6798594
关于积分的说明 15768808
捐赠科研通 5031852
什么是DOI,文献DOI怎么找? 2709267
邀请新用户注册赠送积分活动 1658686
关于科研通互助平台的介绍 1602790