AI-Assisted Drug Discovery: Emerging Technologies and Challenges

Abstract

Artificial Intelligence (AI) is transforming drug discovery by making the process faster, more cost-effective, and more accurate than traditional methods, which often require 10–15 years and billions of dollars to develop a new drug. AI techniques such as machine learning, deep learning, natural language processing, reinforcement learning, and generative AI are widely used for drug target identification, biomarker discovery, molecular screening, toxicity prediction, lead optimization, and clinical trial support. Advanced models including Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformers improve the prediction of molecular properties and drug-target interactions, while generative AI enables the design of novel therapeutic molecules. This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction. Despite its advantages, AI faces challenges including limited high-quality datasets, model bias, interpretability, regulatory uncertainty, computational complexity, and integration with conventional laboratory workflows. The findings indicate that AI significantly improves drug discovery efficiency, reduces research costs, and accelerates pharmaceutical innovation. Future advancements will rely on explainable AI, multimodal biological data integration, federated learning, and stronger regulatory frameworks.