Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches

脚手架 肝损伤 药品 计算机科学 纳米技术 材料科学 药理学 医学 程序设计语言
作者
Yulong Zhao,Zhoudong Zhang,Kai Wang,Jie Jia,Yaxuan Wang,Huanqiu Li,Xiaotian Kong,Sheng Tian
出处
期刊:Research Square - Research Square
标识
DOI:10.21203/rs.3.rs-4268191/v1
摘要

Abstract The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors, and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.
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
潘特发布了新的文献求助10
3秒前
乌滴子完成签到,获得积分10
4秒前
量子星尘发布了新的文献求助10
5秒前
善学以致用应助韭菜盒子采纳,获得10
5秒前
jiaaniu完成签到 ,获得积分10
7秒前
清脆靳完成签到,获得积分10
8秒前
cp3xzh完成签到,获得积分10
8秒前
tian发布了新的文献求助10
10秒前
tian发布了新的文献求助10
10秒前
明理宛秋完成签到 ,获得积分10
11秒前
S月小小完成签到,获得积分10
15秒前
斯文的慕儿完成签到 ,获得积分10
22秒前
keen完成签到 ,获得积分10
22秒前
韭菜盒子完成签到,获得积分20
23秒前
潘特完成签到,获得积分10
24秒前
小彭友完成签到,获得积分10
35秒前
36秒前
josie完成签到 ,获得积分10
40秒前
llll完成签到 ,获得积分10
40秒前
量子星尘发布了新的文献求助10
40秒前
韭菜发布了新的文献求助10
40秒前
外向的斑马完成签到 ,获得积分10
41秒前
村长热爱美丽完成签到 ,获得积分10
43秒前
尹尹关注了科研通微信公众号
45秒前
呆呆完成签到 ,获得积分10
46秒前
xianyaoz完成签到 ,获得积分0
53秒前
杨远杰完成签到,获得积分10
54秒前
蓝桉完成签到 ,获得积分10
54秒前
JuliaWang完成签到 ,获得积分10
1分钟前
无限的含羞草完成签到,获得积分10
1分钟前
八二力完成签到 ,获得积分10
1分钟前
韭菜发布了新的文献求助10
1分钟前
情怀应助科研通管家采纳,获得30
1分钟前
water应助科研通管家采纳,获得10
1分钟前
JamesPei应助科研通管家采纳,获得10
1分钟前
2012csc完成签到 ,获得积分0
1分钟前
风清扬应助韭菜采纳,获得10
1分钟前
WSY完成签到 ,获得积分10
1分钟前
虞无声发布了新的文献求助10
1分钟前
执着新蕾完成签到,获得积分10
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038112
求助须知:如何正确求助?哪些是违规求助? 3575788
关于积分的说明 11373801
捐赠科研通 3305604
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022