Data Quality and Benchmarking Rigor in Machine Learning for Drug Discovery: A Perspective on Aqueous Solubility Modeling

As machine learning (ML) becomes increasingly integrated into drug discovery, reliance on legacy datasets and superficial performance metrics threatens to stall genuine progress. This perspective examines common pitfalls in solubility modeling, specifically overreliance on flawed public datasets and insufficient similarity analysis between training and test sets. By comparing performance on "real-world" datasets with consistent experimental conditions, specifically the Biogen and ASAP Discovery sets, we demonstrate that inflated correlations can mask poor generalizability. We propose new guidelines for authors, reviewers, and journals to elevate the standard of ML validation.