AI-Driven Precision Medicine Leveraging Machine Learning and Big Data Analytics for Genomics-Based Drug Discovery

Mia Md Tofayel Gonee Manik; Sraboni Clara Mohonta; Farhana Karim; Md Alamgir Miah; Md Shafiqul Islam; Md Аsikur Rаhmаn Chy; Muhammad Adnan; Abu Saleh Muhammad Saimon

doi:10.63332/joph.v5i1.1993

Authors

Mia Md Tofayel Gonee Manik College of Business, Westcliff University, Irvine, CA 92614, USA
Sraboni Clara Mohonta Zicklin School of Business, Baruch College, The City University of New York
Farhana Karim Harrison College of Business and Computing, Southeast Missouri State University, Cape Girardeau, Missouri 63701, USA
Md Alamgir Miah School of Business, International American University, Los Angeles, CA 90010, USA
Md Shafiqul Islam Department of Computer Science, Maharishi International University, Fairfield, Iowa 52557, USA
Md Аsikur Rаhmаn Chy School of Business, International American University, Los Angeles, CA 90010, USA
Muhammad Adnan College of Graduate and Professional Studies, Trine University, Detroit, Michigan, USA
Abu Saleh Muhammad Saimon Department of Information Technology, Washington University of Science and Technology, Alexandria VA 22314, USA

DOI:

https://doi.org/10.63332/joph.v5i1.1993

Keywords:

AI-driven precision medicine, machine learning, big data analytics, genomics-based drug discovery, personalized medicine, bioinformatics, drug repurposing, gene-drug interactions, clinical data integration, biomarker identification

Abstract

The clinical use of artificial intelligence for precision medicine delivers transformative results in drug discovery through its combination of big data analytics and machine learning with genomic research. The traditional methods used in the drug discovery process are expensive to implement and take lengthy durations while failing to deliver customized therapy for patients. The advent of artificial intelligence now conducts fast genomic database analysis to find new drug aims and develop targeted medicine for individual patients. The application of ML algorithms enables researchers to make disease progression forecasts as well as identify drug effectiveness and potential adverse effects leading to improved outcomes for genomic drug discovery patients. The research discusses how deep learning and reinforcement learning models are applied to work with big genomic and biomedical information datasets. Data storage through cloud computing platforms together with high-performance computing systems allows precision medicine to scale and become more efficient. AI computational biology combined with clinical information enhances pharma research by optimally structuring drug reuse analysis and promoting new drug development processes. The use of artificial intelligence leads genomics-based drug discovery to a new direction through high efficiency and cost reduction and individual patient therapy delivery. Precision medicine based on AI and bioinformatics advancements push forward personal healthcare into the future despite ongoing obstacles such as data security problems and interpretation challenges and regulatory limitations.