ML-Enabled Drug Discovery: Opportunities and Computational Challenges

Abstract

Machine Learning (ML) has become an indispensable tool in modern drug discovery, offering capabilities to accelerate and enhance the identification of novel therapeutics. Traditional drug discovery is time-consuming, expensive, and prone to high failure rates, but ML methods have demonstrated promising results in analyzing complex biological data, predicting molecular interactions, and generating drug candidates. This research paper explores the multifaceted role of ML in drug discovery, analyzing its opportunities and the significant computational challenges that hinder its full adoption. Through a detailed examination of various ML models and their application in molecular screening, de novo drug design, and optimization, the paper outlines experimental frameworks used in recent studies. It further presents a critical evaluation of challenges such as data scarcity, model interpretability, and computational scalability. Results from comparative experiments illustrate both the potential and limitations of ML-based drug discovery pipelines. The findings suggest a paradigm shift in pharmaceutical research, while underscoring the necessity of addressing computational bottlenecks to ensure robust and reliable outcomes.