Beyond the Algorithm: Artificial Intelligence in Drug Discovery from Code to Clinic

Machine learning (ML) and artificial intelligence (AI) are revolutionizing drug discovery, transforming an inefficient system of trial and error into one based on predictive modeling and data analysis. This comprehensive review on the growing role of computational techniques and tools considers every stage of the traditional drug development pipeline. This paper discusses how novel frameworks, high-throughput data analysis, and molecular dynamics simulations advance the discovery process, from target identification and molecular screening to pharmacokinetic profiling and candidate prioritization for clinical trials. By analyzing a wide range of recent studies, this review demonstrates that these approaches significantly reduce the time required to generate new chemical entities. This paper focuses on the relation between curating data, designing models, and validating experimental results, which together determine the reproducibility and usability of computational outputs. Even though computational methods have yielded many successes, their application is limited by several challenges. Only when paired with high-quality experimental data and interdisciplinary expertise can these methods substantially reduce the time, cost, and uncertainty associated with drug discovery and development. Limitations, including poor data quality, inconsistent data reporting, and limited diversity in chemical and biological databases, continue to constrain accuracy. The lack of interpretability in the results of computational systems complicates regulatory evaluation and broader acceptance within the scientific community. Addressing these challenges involves data standardization, clear benchmarking, and integrating experimental feedback into models. All in all, the findings demonstrate that computational drug discovery is moving from its exploratory phase to a more important role in pharmaceutical research, and future discovery pipelines will increasingly depend on computational frameworks.