Dr Angelo Pugliese, Associate Director of In Silico Discovery at BioAscent, discusses how artificial intelligence can bridge the gap between computational models and the laboratory bench, making AI a practical tool for everyday drug discovery.
Angelo describes how we have collaborated with the University of Stirling to apply machine learning to reduce assay interference from PAINS compounds in high-throughput screening (HTS). By optimising buffer composition using Bayesian optimisation, assay robustness and reliability were improved, reducing false positives, enhancing data quality, and ultimately saving significant time and costs by enabling more confident data-driven decision‑making at an early stage in the drug discovery process.
