Applications of artificial intelligence in pharmaceutical industries

The usage of artificial intelligence (AI) is growing across many societal domains, especially in the pharmaceutical industry. AI has benefits for both the economy and the workplace. It reduces the need for human labor by mimicking human intelligence and fusing it with state-of-the-art technology to produce the finest outcomes. This article looks at the practical uses of artificial intelligence in the Pharmaceutical business across multiple departments and at every level, from medication development to drug discovery. It not only reduces the need for human labor but also increases pharmaceutical output and clinical research efficacy while lowering the risk of error. The paper also examines the present challenges in applying AI in the pharmaceutical sector. It also evaluates present issues and makes recommendations for fixes.

Authors

Publication Details

Journal
International Journal of Pharmacology and Clinical Research
Published
2025-01-01
DOI
https://doi.org/10.33545/26647613.2025.v7.i1c.76
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Applications of artificial intelligence in pharmaceutical industries

Gaurav Patil, Arghya Das Dev
International Journal of Pharmacology and Clinical Research
Computational Drug Discovery Methods
article

Applications of artificial intelligence in pharmaceutical industries

Gaurav Patil, Arghya Das Dev
article en

Abstract

The usage of artificial intelligence (AI) is growing across many societal domains, especially in the pharmaceutical industry. AI has benefits for both the economy and the workplace. It reduces the need for human labor by mimicking human intelligence and fusing it with state-of-the-art technology to produce the finest outcomes. This article looks at the practical uses of artificial intelligence in the Pharmaceutical business across multiple departments and at every level, from medication development to drug discovery. It not only reduces the need for human labor but also increases pharmaceutical output and clinical research efficacy while lowering the risk of error. The paper also examines the present challenges in applying AI in the pharmaceutical sector. It also evaluates present issues and makes recommendations for fixes.

International Journal of Pharmacology and Clinical ResearchVol. 7(1)
Openalex Percentile: Top 85%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.