CADD, AI and ML in drug discovery: A comprehensive review

被引：104

作者：

Vemula, Divya ^{[1
]}

Jayasurya, Perka ^{[1
]}

Sushmitha, Varthiya ^{[1
]}

Kumar, Yethirajula Naveen ^{[1
]}

Bhandari, Vasundhra ^{[1
,2
]}

机构：

[1] Natl Inst Pharmaceut Educ & Res Hyderabad, Hyderabad, India

[2] Natl Inst Pharmaceut Educ & Res, Dept Pharmacoinformat, Hyderabad 500037, Telangana, India

来源：

EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES | 2023年 / 181卷

关键词：

Computer-aided drug design; Artificial intelligence; Machine learning; Deep learning; drug discovery and development; MOLECULAR-DYNAMICS SIMULATIONS; FLEXIBLE LIGAND DOCKING; MULTIPLE RECEPTOR CONFORMATIONS; PROTEIN-STRUCTURE PREDICTION; FORCE-FIELD; AUTOMATED DOCKING; GENETIC ALGORITHM; NEURAL-NETWORKS; LEARNING APPROACH; NUCLEIC-ACIDS;

D O I：

10.1016/j.ejps.2022.106324

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

引用

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