AI Models for Predicting Drug Efficacy and Toxicity to Accelerate Personalized Medicine and Drug Discovery

Today, Artificial Intelligence (AI) is ushering in early drug discovery and precision medicine due to its abilities to increase the efficiencies of the drug process. Conventional approaches to drug development are time-consuming and time which is often more than ten years is required to get a new drug to patients. The existing landscape of drug discovery has been revolutionized by the application of AI, ML, as well as big data analytics. With help of AI models like the one from the CUNY Graduate Centre providing an opportunity to analyze large data sets in order to identify potential drugs, as well as predict the effectiveness and toxicity of such drugs, absence of high–throughput virtual screening substantially amplifies the costs and time required for the development of new drugs. These models mimic the effects of drugs on biological targets meaning that the general success rates in clinical trials are improved. In addition, AI leads to the enhancement of patients care through the customization of drugs and dosage forms according to the patients’ genes, lifestyle and environment. The complex genetic data are analyzed by the AI systems and biomarkers of patients’ reaction to the medications are determined, so the personalized approach to the treatment can be introduced. AI also anticipates and prevents drug toxicity and interactions to reduce bad effects on the patient’s health. The study highlights the critical factors influencing the integration of AI in drug discovery: Concerning, Data Quality and Quantity, Integration with Existing Systems, Algorithmic Transparency and Interpretability, and Regulation and Ethical Issues, Computational Power, and Interdisciplinary Approach and Collaboration. Results derived from ANOVA, Chi-Square, and One-Sample t-tests accentuate the equal significance of these factors the need to go further with the process of enhancement. Thus, the study states that the importance of these factors are recognized but unique approaches are need for the use of AI in precision medicine.