Role of artificial intelligence and machine learning in drug discovery, personalised treatment, and simplifying medical treatment
Rinku Manvani, Harsh Purohit, Bhumika Choksi, Chita Ranjan Sahoo, Sejal K. Shah · 2025
Artificial intelligence (AI) and machine learning (ML) have become more important in drug discovery, personalised treatment, and diagnostic purposes. No personalized approaches are entertained in classical methods of medical treatment. However, AI and machine learning tools bridge experimental techniques with computational techniques and make the task much easier, reliable, and accurate. Implementation of AI and ML tools facilitates drug discovery (drug repurposing and virtual screening) and personalized medication by employing several methods such as Artificial Neural Network (ANN), DeepSea, Support Vector Machine (SVM), Naive Bayesian Network, Random Forest, Linear Regression, Deep Learning, Human Splicing Code and Hidden Markov Model (HMM). Advancements in genomics and transcriptomics using genome modifying technologies (CRISPR-cas9) and antisense technology helped clinicians in the identification of various biological markers, clinical history, disease symptoms, and etiology in genomic and non-genomic determinants, facilitating diagnosis and prognosis (as early prediction of cancer stages). AI uses complex processing and reasoning to generate ideas, allow the system to process information and make decisions, and improve clinical decision-making. According to current literary work, clinical studies targeting this integration will help to resolve many of the challenging obstacles facing precision medicine. The utilization of AI in healthcare delivery faces unique hurdles, in addition to contemporary AI systems’ severe technical limitations in contrast to human vision, language understanding, and context-specific reasoning. This chapter focuses on the various aspects and roles of AI and ML in accelerating the drug discovery process using simulations and mathematical modelling to simplify and augment the accuracy of personalised treatment, diagnosis, and prognosis.