Machine Learning Approaches for Enhancing Drug Discovery and Development in Healthcare
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Keywords

Drug Discovery
Healthcare
Pharmaceutical Research
Clinical Trials
Surgical Robotics

How to Cite

[1]
Dr. Helena Santos, “Machine Learning Approaches for Enhancing Drug Discovery and Development in Healthcare: AI Models for Accelerating Pharmaceutical Research and Clinical Trials”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 2, pp. 277–291, Sep. 2023, Accessed: Nov. 13, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/108

Abstract

Drug discovery and development underpin the healthcare system and have a significant impact on the modern world. A wide range of complex diseases and symptoms arising from genetic traits and multi-omics interactions raises the bar for supervised pharmaceutical research and development. Usually, it takes over 12 to 15 years to introduce new drugs from early development to the commercial market, with luck to gain market approval. As a result of implementing guidelines and global pharmaceutical standard practices, the R&D process to access innovation between large companies, start-ups, and research centers is systematic, which involves launching drug-related R&D projects, biological systems, compound libraries, high-throughput and high-content screening, hit-to-lead and lead optimization, in vitro and in vivo evaluation, clinical trials, pharmacokinetics, absorption, and pharmacodynamics, toxicity testing, scientific advisory review, and market approval. Despite this, obtaining market approval remains a significant challenge for drug developers. Innovative approaches such as phenotypic screening and authorized therapy in healthcare are required. Therefore, to cope with these healthcare challenges, it is essential for pharmaceutical researchers and manufacturers to introduce high-throughput screening and early identification approaches that can accelerate new and helpful innovations by developing effective and safe drugs.

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