Accelerated Hit Identification with Target Evaluation, Deep Learning and Automated Labs: Prospective Validation in IRAK1

In this study, we synergistically integrate Ro5's target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with Strateos automated robotic cloud lab optimized for ultra high-throughput screening, to experimentally validate Ro5's tools. This integrated approach leads to a significant acceleration in the processes of target identification and hit discovery. By using SpectraView to select IRAK1 as the focal point of our investigation, we prospectively validate HydraScreen structure-based deep learning model. We can achieve the identification of an 23.8% of all IRAK1 hits within the top 1% of ranked compounds. HydraScreen also outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, two of which represent novel candidates for IRAK1 and hold potential for future development. Our platforms and innovative tools promise to expedite the early stages of drug discovery.