RIDEB CHAKRABORTY

Mr. RIDEB CHAKRABORTY

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 July 2025 in International Journal of Biological Macromolecules
Verified
Journal Article
Closed access
NANaureen Afrose
Naureen Afrose
RIDEB CHAKRABORTYRIDEB CHAKRABORTY
RIDEB CHAKRABORTY
KRKavitha Rajendran
Kavitha Rajendran


14 April 2023 in Journal of Drug Delivery Science and Technology
Verified
Journal Article
Closed access
RIDEB CHAKRABORTYRIDEB CHAKRABORTY
RIDEB CHAKRABORTY
NANaureen Afrose
Naureen Afrose
KKKETOUSETUO KUOTSU
KETOUSETUO KUOTSU



30 May 2024
Verified
Book Chapter
Closed access
NANaureen Afrose
Naureen Afrose
RIDEB CHAKRABORTYRIDEB CHAKRABORTY
RIDEB CHAKRABORTY
AHAhana Hazra
Ahana Hazra
PBPratibha Bhowmick
Pratibha Bhowmick
MBMITHUN BHOWMICK
MITHUN BHOWMICK
Artificial intelligence (AI) has revolutionized the discovery and development of new drugs in biomedicine. By using advanced algorithms and computational methods, AI optimizes treatment plans, accelerates the drug development process, and improves patient outcomes. AI algorithms integrate multi-omics data sets, decipher molecular connections, and identify therapeutic targets and biomarkers. High-throughput screening, predictive modeling, and AI-powered virtual screening platforms are revolutionizing the drug development pipeline. Machine learning and deep learning models enable drug-target interactions prediction, pharmacological evaluation, and experimental validation. Structure-based drug design methodologies accelerate the discovery of new therapies. AI-driven technologies enable personalized treatment plans for patients, taking into account their unique traits and disease profiles. Pharmacogenomics, when combined with predictive analytics, improves drug selection, dosage adjustment, and treatment response prediction, enhancing therapeutic efficacy and reducing side effects.