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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63730
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANAGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANAGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANAGERE
Artificial Intelligence (AI) is transforming the field of drug discovery by accelerating the identification, design, and development of new therapeutic compounds. Traditional drug discovery is a time-consuming and expensive process that often takes more than a decade and billions of dollars to bring a single drug to market. AI techniques such as machine learning, deep learning, and natural language processing enable researchers to analyse vast biological and chemical datasets efficiently. By leveraging data from genomic studies, clinical trials, and biomedical literature, AI models can predict drug–target interactions, identify potential biomarkers, and optimize lead compounds with higher accuracy and reduced experimental cost.AI-driven platforms are widely adopted by pharmaceutical companies and research institutions to improve success rates in preclinical and clinical stages. For example, organizations like Deep Mind and IBM Watson Health have contributed to protein structure prediction and data-driven medical research, enhancing target identification and drug repurposing strategies. Furthermore, AI supports personalized medicine by analysing patient-specific data to recommend tailored treatments. Despite challenges such as data quality, model interpretability, and regulatory concerns, AI continues to revolutionize drug discovery by reducing development timelines, minimizing costs, and increasing the probability of successful therapeutic outcomes.
KEYWORDS
AI for Drug Discovery” is a timely and transformative initiative designed to address long-standing challenges in the pharmaceutical industry. Traditional drug discovery approaches are often costly, time-consuming and uncertain, requiring years of research with no guarantee of success. Moreover, every stage of the process demands continuous technological advancement and significant human effort. With the rise of Artificial Intelligence a new paradigm in drug development is emerging. AI-driven drug discovery is not just an innovation—it has become a practical necessity to overcome existing barriers in pharmaceutical research. Advanced algorithms, predictive models and large-scale data analytics now allow researchers to analyse complex biological data with unprecedented speed and accuracy. The proposed AI-based platform empowers scientists to identify hidden therapeutic targets more efficiently and supports early, accurate assessment of drug safety and efficacy. By reducing uncertainty in the initial stages, the platform significantly increases the likelihood of successful outcomes in later clinical trials. The successful implementation of this project promises far-reaching benefits. For the pharmaceutical industry it offers reduced development costs, shorter timelines, higher clinical success rates and a stronger pipeline of innovative therapies. While challenges remain the potential of the platform far outweighs its limitations. The approach is both technically feasible and economically sustainable. AI in drug discovery is poised to play a defining role in the future of healthcare by fundamentally transforming how medicines are researched, developed and delivered.
[1] Ekins S. Puhl A.C. Zorn K.M. et al. (2019). Exploiting Machine Learning for End-to-End Drug Discovery. Nat. Mater. 18 435–441. [2] Schneider G. (2018). Automating Drug Discovery. Nat. Rev. Drug Discov. 17 97–113. [3] Stokes J.M. Yang K. Swanson K. et al. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell 180(4) 688–702. [4] Mamoshina P. Vieira A. Putin E. Zhavoronkov A. (2016). Applications of Deep Learning in Biomedicine. Mol. Pharm. 13 1445–1454. [5] Li X. Fourches D. (2020). Inductive Transfer Learning for Molecular Activity Prediction: A Review. J. Chem. Inf. Model. 60 5580–5597
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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