Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization

限制 背景(考古学) 化学空间 药物发现 配体效率 小分子 生化工程 化学结构 计算机科学 化学 计算生物学 组合化学 生物系统 纳米技术 配体(生物化学) 材料科学 生物 工程类 生物化学 有机化学 受体 古生物学 机械工程
作者
Maximilian Beckers,Finton Sirockin,Nikolas Fechner,Nikolaus Stiefl
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
卷期号:64 (16): 6636-6647 被引量:3
标识
DOI:10.1021/acs.jcim.4c00898
摘要

Chemical structure optimization is a vital part of early drug discovery projects. Starting with compounds that show activity on the target of interest, the chemical structures are subsequently optimized toward a development candidate (DC) molecule with the best chances of clinical success. However, the DCs in the context of such optimization programs, as well as detailed characterization of major limiting factors, have not been investigated in detail so far. Here, we report an analysis of the historical DC molecules at Novartis since 2005 in the context of their optimization projects. Mapping the DCs into their respective chemical optimization series, we find that these tend to be synthesized rather early in a substantial number of cases. Further analysis of structural properties, ADMET, and potency-related readouts revealed that DC compounds tend to be generally significantly smaller, more permeable, and have higher ligand efficiency than other compounds sent to in vivo PK studies, which we also show for compounds from the same chemical series. Although this might seem obvious to most practitioners in medicinal chemistry, for all of these properties, we could show that they tend to evolve in an undesired direction during structure optimization. This highlights the difficulty of successfully translating our knowledge to medicinal chemistry optimizations.
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
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
科研通AI6.4应助aimme采纳,获得10
1秒前
纳古菌完成签到,获得积分10
1秒前
AveryZhang完成签到,获得积分10
1秒前
干净的南蕾完成签到,获得积分20
2秒前
cocofan发布了新的文献求助10
2秒前
笨笨的誉完成签到,获得积分10
3秒前
3秒前
元昭诩完成签到,获得积分10
3秒前
猫和老鼠发布了新的文献求助10
3秒前
zbzb发布了新的文献求助10
5秒前
7秒前
7秒前
FashionBoy应助科研通管家采纳,获得10
7秒前
领导范儿应助科研通管家采纳,获得10
7秒前
Ava应助科研通管家采纳,获得10
7秒前
Orange应助科研通管家采纳,获得10
7秒前
东方元语应助科研通管家采纳,获得20
8秒前
8秒前
Orange应助科研通管家采纳,获得10
8秒前
Orange应助科研通管家采纳,获得10
8秒前
8秒前
1104481279应助科研通管家采纳,获得10
8秒前
8秒前
A阿澍完成签到,获得积分10
9秒前
无花果应助科研通管家采纳,获得10
9秒前
东方元语应助科研通管家采纳,获得20
9秒前
赘婿应助YYYBGGHJU采纳,获得10
9秒前
10秒前
田様应助虚心的灵寒采纳,获得10
11秒前
正月初九发布了新的文献求助10
11秒前
Nivas完成签到,获得积分10
11秒前
2256282919完成签到,获得积分10
11秒前
赵玉蔓完成签到,获得积分10
11秒前
12秒前
HZH完成签到,获得积分10
12秒前
wangping发布了新的文献求助10
13秒前
熊大发布了新的文献求助30
13秒前
完美世界应助tianqing采纳,获得10
13秒前
钟钟完成签到 ,获得积分10
14秒前
15秒前
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
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Synthesis of P-Chiral Phosphine Ligands and Their Applications in Asymmetric Catalysis 400
Management and the Arts 310
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7629518
求助须知:如何正确求助?哪些是违规求助? 9203974
关于积分的说明 19736300
捐赠科研通 7199027
什么是DOI,文献DOI怎么找? 3274277
关于科研通互助平台的介绍 2436423
邀请新用户注册赠送积分活动 2270424