The Role of Artificial Intelligence in Drug Design: Simulation, Optimization, and Development

The process of drug design is a complex, multi-stage endeavor that integrates experimental, clinical, and computational models to identify and develop novel therapies. Despite significant advancements in biotechnology and understanding of biological systems, this process continues to face challenges such as high costs, time-intensive procedures, and low success rates. The advent of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized this field. These technologies accelerate drug discovery and design by analyzing complex datasets and uncovering hidden patterns, thereby enhancing predictive accuracy. Applications of AI in drug design encompass molecular interaction simulations, biological effect predictions, and drug property optimization. By simulating chemical structures and predicting properties such as toxicity, binding affinity, and compound stability, AI has significantly contributed to improving drug quality. Additionally, AI plays a pivotal role in designing prodrugs and developing therapeutic antibodies, reducing side effects, and enhancing the pharmacokinetic properties of drugs. This article explores AI applications in drug design and development, focusing on innovative models such as graph neural networks and deep learning. Recent advancements and future opportunities for leveraging these technologies to accelerate drug design are also examined. The adoption of these approaches promises to reduce costs, improve success rates, and enhance the quality of novel therapies.

Keywords:

Artificial intelligence , drug design , optimization of drug properties , modeling , simulation of molecular interactions