ABSTRACT:
The integration of Artificial Intelligence (AI) in lead identification for drug discovery represents a transformative advancement in pharmaceutical research. Traditional drug discovery processes, characterized by high costs and lengthy timelines, have been significantly enhanced by AI technologies. This review provides a detailed examination of how AI methodologies, including machine learning (ML), deep learning (DL), and reinforcement learning (RL), are revolutionizing lead identification. ML techniques, such as Quantitative Structure-Activity Relationship (QSAR) models and virtual screening, enable the prediction of biological activity and optimization of molecular structures. Deep learning approaches, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), facilitate the analysis of complex molecular data and the generation of novel compounds with desirable properties. RL methods are employed to refine lead compounds through iterative optimization processes. This review also highlights the application of AI in predictive modeling of bioactivity, drug repurposing, target identification, and multi-objective optimization. Despite the promising advances, challenges such as data quality, model interpretability, and computational resource requirements remain. Future directions include the development of explainable AI models, integration of multiomics data, and the advancement of personalized medicine approaches. This comprehensive overview underscores the significant impact of AI in accelerating lead identification and improving drug discovery outcomes.