Application of Artificial Intelligence in Drug Discovery

被引：9
|
作者：
Chopra, Hitesh [1 ]
Baig, Atif A. [2 ]
Gautam, Rupesh K. [3 ]
Kamal, Mohammad A. [4 ,5 ,6 ,7 ,8 ]
机构：
[1] Chitkara Univ, Chitkara Coll Pharm, Rajpura 140401, Punjab, India
[2] Univ Sultan Zainal Abidin, Fac Med, Dept Pharmacol, Unit Biochem, Kuala Terengganu 20400, Malaysia
[3] Maharishi Markandeshwar Univ, MM Sch Pharm, Sadopur Ambala 134007, India
[4] Sichuan Univ, West China Hosp, Frontiers Sci Centerfor Dis Related Mol Network, Inst Syst Genet, Chengdu, Peoples R China
[5] King Abdulaziz Univ, King Fahad MedicalRes Ctr, Jeddah, Saudi Arabia
[6] Daffodil Int Univ, Fac Allied Hlth Sci, Dept Pharm, Dhaka, Bangladesh
[7] Enzymoics, 7 Peterlee Pl, Hebersham, NSW 2770, Australia
[8] Novel Global Commun Educ Fdn, Sydney, NSW, Australia
来源：
CURRENT PHARMACEUTICAL DESIGN | 2022年 / 28卷 / 33期
关键词：
Artificial intelligence; drug discovery; high-throughput screening; electronic records; molecular docking; machine learning; deep learning; DATA-BANK HSDB; NEURAL-NETWORKS; GENETIC ALGORITHM; SCORING FUNCTION; PREDICTION; TOXICITY; QSAR; OPTIMIZATION; RECOGNITION; ADAPTATION;
D O I：
10.2174/1381612828666220608141049
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery based on big data to precised, knowledgeable data. The pharmaceutical companies have already geared their departments for this and started a race to search for new novel drugs. The AI helps to predict the molecular structure of the compound and its in-vivo vs. in-vitro characteristics without hampering life, thus saving time and economic loss. Clinical studies, electronic records, and images act as a helping hand for the development. The data mining and curation techniques help explore the data with a single click. AI in big data analysis has paved the red carpet for future rational drug development and optimization. This review's objective is to familiarise readers with various advances in the AI field concerning software, firms, and other tools working in easing out the labor of the drug discovery journey.
引用
收藏
页码：2690 / 2703
页数：14
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