The search for bioactive compounds is the key step in early drug discovery. Among other techniques, the similarity principle (in the form of matched molecular pairs or free energy prediction), structure-based virtual screening, and of course experimental high-throughput screening are applied. In this talk, our results related to the use of deep learning and other machine learning techniques in these three design scenarios are summarized. How well does machine learning on matched molecular pairs affinity data perform? What signals do deep learning-based scoring functions for protein-ligand docking capture and how do they compare to classical ML methods? How can we make use of ML in the evaluation of experimental screening data? In summary, we will demonstrate that standard validation schemes on frequently used data sets like DUD-E are non-conclusive in many cases. It remains a challenge for the future to develop novel validation procedures and datasets to truly compare ML-based methods for bioactivity prediction.<br/>

Machine learning in the context of bioactivity

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.