AI Powered Identification of Drug Targets and Pathways for Diagnosis and Treatment Planning: A Review

Abstract

AI has become an integral part of drug discovery, particularly in the identification of drug targets and pathways for diagnosis and treatment planning. By using machine learning algorithms to analyze large datasets, AI can identify potential drug targets and predict drug efficacy, potentially streamlining the drug development process and improving patient outcomes. In this article, we have discussed the emerging role of AI in the discovery of drug targets and pathways for diagnosis and treatment planning. We have explored how AI is being used to identify potential drug targets by analyzing large-scale genomic and proteomic data. Additionally, we have discussed how AI can predict drug efficacy by analyzing patient data, leading to more personalized treatment plans and improved patient outcomes. We also highlighted the use of AI in biomarker discovery and some challenges in the implementation of AI in drug discovery, such as the need for large amounts of high-quality data and the interpretability of AI-generated results.