Shubham Gurule*
Pratik Bhabad
Anuja Darade
The term "artificial intelligence" was given by John McCarthy at the Dartmouth Convention in 1956 to describe "the science and engineering of intelligent machines"[1]. The pharmaceutical industry conducts drug research, development, production, and distribution. The pharmaceutical value chain begins with drug discovery, the process of identifying novel therapeutic candidates. During the drug development process, a drug candidate undergoes preclinical study before becoming a clinically meaningful drug. Clinical trials are undertaken to ensure safety, efficacy, dose, and tolerance [2]. If the clinical study is deemed substantial and successful, the pharmaceutical company will submit a new drug application (NDA) to the regulatory body for approval after a thorough assessment of the findings. Drug discovery is costly, time-consuming, and frequently unsuccessful. Molecules typically take 10-12 years from discovery to market[3]. Enhanced treatments that offer incremental improvements over current medications are crucial, as they can enhance aspects of existing drugs like effectiveness, safety, tolerability, or convenience. However, these improvements typically do not involve alterations to biological targets that differ from those directly impacted by the existing therapies[4]. Artificial intelligence is commonly utilized in healthcare for the following purposes:
· Research
· Digital health monitoring and diagnostics
· Patient data & risk analysis
· Surgery
· Mental health
· Hospital Management
· Virtual assistant
· Drug discovery
· Wearable.
Principle Of AI
Life-science problems can be solved by any method or technology that offers conventional statistical, mathematical, and veterinary methods that are ineffectual or inefficient[5]. Information management, AI machine learning, and multi-agent systems can all significantly impact how experiments are carried out[6]. Technical assistance for integrating and developing human and robot capabilities can be obtained from the domains of agencies, natural language processing, vision, syntax, and human-computer interface[7]. The foundation for accessible discovery papers and the ranking of bioactive compounds according to their effectiveness as drug-like leads and the intended pharmacological effects are provided by machine learning[8]. These days, new fields of protein design application and biological target discovery are developing. Chemocentric techniques have become widely used in numerous molecular informatics machine learning systems[9].
AI in the lifecycle of pharmaceutical products
Given that AI can support logical medication design, its involvement in the pharmaceutical product development process from the bench to the bedside is conceivable[10].
10.5281/zenodo.15078083