Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.
- 1Department of Biotechnology, School of Biosciences and Technology Vellore Institute of Technology, Vellore, India.
- 2Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, India.
Abstract
The practice of artificial intelligence has recently expanded several social spheres, with the pharmaceutical domain leading the way. Given that AI can facilitate rational drug design, it is plausible to envision its involvement in developing pharmaceutical products from the laboratory to the patient's bedside. AI recognizes hit and lead compounds, followed by validation of therapeutic targets and optimization of molecular design. Artificial intelligence significantly impacts drug discovery and development processes, including target identification, molecule design, drug-drug interactions, drug repurposing, optimization of clinical trials, drug toxicity prediction, drug formulation, and delivery. AI-assisted approaches are progressively being employed for identification and prioritization of targets, biomarker discovery and virtual screening as well as prediction for the interaction of protein and ligand and their molecular properties followed by ADMET screening, molecular design and repurposing to be suited for clinical trial optimization. The significance of automation will be boosted by adopting the most recent artificial intelligence-based technologies, reducing the time it takes for drugs to be available in the market with improved quality, production process safety, and resource utilization. The application of artificial intelligence may not only aid in rapid decision-making but can also enhance the process of incorporating the newly discovered drug into the optimum dosage form, which leads to faster manufacturing of higher-quality drugs and ensures consistency among batches. The potential of artificial intelligence in drug discovery and development, has been explored in this review, encircling several stages from identification of target to its clinical applications, followed by drug development approaches encompassing the future of artificial intelligence in the pharmaceutical sector, along with crosstalk on the tools and tactics used to enforce artificial intelligence, current difficulties, and respective solutions.
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BibTeX
@article{de2026redefining,
title = {{Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.}},
author = {Diptanu De and Sohini Chakraborty and Satarupa Banerjee},
journal = {Frontiers in oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1907591},
url = {https://europepmc.org/article/MED/42898694},
note = {CC BY},
}
RIS
TY - JOUR
TI - Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.
AU - Diptanu De
AU - Sohini Chakraborty
AU - Satarupa Banerjee
PY - 2026
DA - 2026/10/05
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1907591
UR - https://europepmc.org/article/MED/42898694
AB - The practice of artificial intelligence has recently expanded several social spheres, with the pharmaceutical domain leading the way. Given that AI can facilitate rational drug design, it is plausible to envision its involvement in developing pharmaceutical products from the laboratory to the patient's bedside. AI recognizes hit and lead compounds, followed by validation of therapeutic targets and optimization of molecular design. Artificial intelligence significantly impacts drug discovery and development processes, including target identification, molecule design, drug-drug interactions, drug repurposing, optimization of clinical trials, drug toxicity prediction, drug formulation, and delivery. AI-assisted approaches are progressively being employed for identification and prioritization of targets, biomarker discovery and virtual screening as well as prediction for the interaction of protein and ligand and their molecular properties followed by ADMET screening, molecular design and repurposing to be suited for clinical trial optimization. The significance of automation will be boosted by adopting the most recent artificial intelligence-based technologies, reducing the time it takes for drugs to be available in the market with improved quality, production process safety, and resource utilization. The application of artificial intelligence may not only aid in rapid decision-making but can also enhance the process of incorporating the newly discovered drug into the optimum dosage form, which leads to faster manufacturing of higher-quality drugs and ensures consistency among batches. The potential of artificial intelligence in drug discovery and development, has been explored in this review, encircling several stages from identification of target to its clinical applications, followed by drug development approaches encompassing the future of artificial intelligence in the pharmaceutical sector, along with crosstalk on the tools and tactics used to enforce artificial intelligence, current difficulties, and respective solutions.
SN - 2234-943X
ER -