Utilizing Artificial Intelligence in the Development of Medicinal Drugs: Practical Applications

Abstract

The COVID-19 pandemic has underscored the critical need for innovative drug discovery methods. The journey from conceptualizing a drug to its clinical application is fraught with potential pitfalls, including extensive complexity, significant expenses, and a high risk of failure. Recent years have witnessed remarkable advancements in technologies such as cloud computing, GPUs, and TPUs. These developments, coupled with the surge in medical data availability and the emergence of deep learning, present an unprecedented opportunity to enhance drug discovery processes. By leveraging artificial intelligence (AI) to analyze vast amounts of data-from extensive molecular screening outcomes to individual health records and public health data-the efficiency of the drug discovery pipeline could be significantly improved, minimizing the likelihood of failure. This paper explores the application of AI in various phases of drug development, including the use of computational strategies for de novo drug design and the prediction of drug properties. We address challenges associated with molecular representation, data acquisition, complexity, and the inconsistencies in labeling across open-source databases and AI-powered tools that support drug discovery efforts. Furthermore, we examine the role of advanced AI techniques, such as graph neural networks, reinforcement learning, generative models, and structure-based methods like molecular docking and dynamics simulations, in enhancing drug discovery and evaluating drug efficacy.