Evaluation of Artificial Intelligence and Algorithms in Drug Discovery

Abstract

The integration of Artificial Intelligence (AI) and advanced algorithms has significantly transformed the drug discovery process by enabling faster, cost-effective, and data-driven approaches. Traditional drug development is time-consuming, expensive, and characterized by high failure rates, necessitating innovative solutions. AI techniques, including machine learning, deep learning, natural language processing, and reinforcement learning, play a crucial role in various stages of drug discovery such as target identification, virtual screening, lead optimization, and clinical development. Algorithms like Naïve Bayes, Support Vector Machines, Decision Trees, Random Forest, and boosting methods enhance predictive accuracy and decision-making capabilities. Additionally, deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and Graph Neural Networks facilitate the analysis of complex biological and chemical data. Despite these advancements, challenges such as data quality, model interpretability, and integration into existing pharmaceutical frameworks remain significant barriers. Overall, AI-driven methodologies hold great promise in improving efficiency, reducing costs, and accelerating the development of safe and effective therapeutic agents.