Artificial Intelligence in De novo Drug Design: Are We Still There?

计算机科学 人工智能 机器学习 药物开发 药品 药物发现 化学空间 领域(数学) 数据科学 风险分析(工程) 医学 生物信息学 药理学 生物 数学 纯数学
作者
Rajnish Kumar,Anju Sharma,Αθανάσιος Αλεξίου,Ghulam Md Ashraf
出处
期刊:Current Topics in Medicinal Chemistry [Bentham Science]
卷期号:22 (30): 2483-2492 被引量:2
标识
DOI:10.2174/1568026623666221017143244
摘要

The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation.The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce.In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field.The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
伶俐的雁蓉完成签到,获得积分10
刚刚
2秒前
luyao970131发布了新的文献求助10
2秒前
2秒前
chen发布了新的文献求助10
5秒前
乐乐应助cindy采纳,获得10
5秒前
Liben完成签到,获得积分10
9秒前
热心子轩发布了新的文献求助10
10秒前
gg发布了新的文献求助20
11秒前
Tici完成签到,获得积分10
11秒前
123完成签到 ,获得积分10
11秒前
鬼鬼的眼睛完成签到,获得积分10
12秒前
狗蛋发布了新的文献求助20
13秒前
14秒前
科研通AI2S应助冷静的莞采纳,获得10
14秒前
16秒前
高兴的灵雁完成签到 ,获得积分10
18秒前
王小妖完成签到 ,获得积分10
19秒前
20秒前
20秒前
烟雨完成签到,获得积分10
22秒前
22秒前
害怕的擎宇完成签到,获得积分10
24秒前
科研通AI2S应助chen采纳,获得10
25秒前
27秒前
贪玩电源完成签到,获得积分10
27秒前
星辰大海应助淡淡菠萝采纳,获得10
28秒前
28秒前
维夏十一完成签到,获得积分10
30秒前
30秒前
semiaa完成签到,获得积分10
31秒前
乐乐应助多宝鱼儿采纳,获得10
31秒前
LD完成签到 ,获得积分10
32秒前
34发布了新的文献求助10
32秒前
33秒前
情怀应助破忒头采纳,获得10
35秒前
赵西里完成签到,获得积分10
35秒前
烟雨关注了科研通微信公众号
35秒前
36秒前
36秒前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Chen Hansheng: China’s Last Romantic Revolutionary 500
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3140687
求助须知:如何正确求助?哪些是违规求助? 2791539
关于积分的说明 7799401
捐赠科研通 2447880
什么是DOI,文献DOI怎么找? 1302124
科研通“疑难数据库(出版商)”最低求助积分说明 626459
版权声明 601194