Drug Res (Stuttg) 2024; 74(05): 208-219
DOI: 10.1055/a-2306-8311
Review

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

Mukhtar Lawal Abubakar
1   School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
,
Neha Kapoor
1   School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
,
Asha Sharma
2   Department of Zoology, Swargiya P. N. K. S. Govt. PG College, Dausa, Rajasthan, India
,
Lokesh Gambhir
3   School of Basic and Applied Sciences, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India
,
Nakuleshwar Dutt Jasuja
4   School of Basic and Applied Sciences, Nirwan University, Jaipur, Rajasthan India
,
Gaurav Sharma
1   School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
› Institutsangaben
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Abstract

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.



Publikationsverlauf

Eingereicht: 22. Januar 2024

Angenommen: 02. April 2024

Artikel online veröffentlicht:
03. Juni 2024

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