Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research

Abstract

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like 'artificial intelligence', 'drug discovery', 'pharmacy research', 'clinical trial', etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services.

Keywords: artificial intelligence; computational learning; drug discovery; healthcare; patient; personalized medicine; structural activity relationship.

Figure 1

History of artificial intelligence in healthcare.

Figure 2

Varieties of AI machine learning algorithms, and both regular and free of control, with an emphasis on pharmaceuticals.

Figure 3

Process of Drug discovery with the help of AI.

Figure 4

AI in drug discovery.

Figure 5

Approaches in drug discovery.

Figure 6

AI in acquiring and analyzing data of a patient in personalizing the treatment.

Figure 7

System architecture for illness detection.

Figure 8

AI can improve AI can improve the design of nanosystem, broaden the drug assessment modelling, improve features and aspects for choosing a particular design, exploration, and recycling. Drug penetration, modelling, cell targets, etc. assist explain transmembrane connection with the modelled human milieu.

Figure 9

Role of AI in PKPD studies. Pharmacokinetic studies include absorption (A), distribution (D), metabolism (M), and excretion (E) studies.