AI-Driven Drug Discovery: A Critical Evaluation of Model Reliability, Data Bias, and Clinical Translation

Abstract

Artificial Intelligence (AI) is rapidly transforming the landscape of drug discovery by enabling faster and more efficient identification of potential therapeutic candidates. Advanced computational techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have demonstrated significant potential in analyzing complex biological datasets, predicting drug-target interactions, and optimizing lead compounds. Despite these advancements, several critical challenges hinder the widespread adoption of AI in pharmaceutical research. Model reliability remains a major concern due to issues such as overfitting, limited generalizability, and lack of interpretability. Additionally, biases present in training datasets can result in skewed predictions, thereby affecting the safety and efficacy of AI-generated drug candidates. Furthermore, the translation of AI-based discoveries from computational models to clinical applications continues to face substantial barriers, including regulatory constraints and validation complexities. This review critically examines these challenges, emphasizing the need for robust validation strategies, high-quality datasets, and interdisciplinary collaboration. Addressing these limitations is essential for realizing the full potential of AI in developing safe, effective, and accessible therapeutics.