Deep Learning vs Classical Methods in Potency & ADME Prediction: Insights from a Computational Blind Challenge

Reliable prediction of compound potency and ADME profile is crucial in drug discovery. With the recent surge of AI and deep learning frameworks, it remains unclear whether these modern techniques offer statistically significant improvement over the well-established classical methods. The 2025 ASAP-Polaris-OpenADMET Antiviral Challenge provided a unique benchmarking opportunity to address this question, joined by over 65 teams of computational scientists worldwide. Our submissions were among the top performers in terms of Pearson r correlation, ranked first in pIC50 prediction for SARS-CoV-2 Mpro and fourth in aggregated ADME. In this work, we present a retrospective analysis of our modeling strategies and highlight our lessons learned. Through rigorous statistical benchmarking, we demonstrate that while classical methods remain highly competitive for predicting potency, modern deep learning algorithms significantly outperformed traditional machine learning in ADME prediction. We also illustrate the importance of appropriate data curation and the benefits of leveraging public datasets via feature augmentation. Finally, we outline current limitations and identify future opportunities, including the integration of structure-guided modeling. Overall, these results not only provide practical guidance for building robust predictive models but also offer valuable insights in the field of computational drug discovery.