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

In this study, we integrate Ro5’s target evaluation SpectraView and DL-driven virtual screening HydraScreen tools alongside Strateos' robotic cloud labs high-throughput screening platform to accelerate target and hit identification. Using SpectraView to select IRAK1 as the target, we prospectively validate HydraScreen, a structure-based deep learning model. We demonstrate that HydraScreen could identify up to 23.8% of all IRAK1 hits in the top 1% of the ranked compounds, simultaneously identifying the three most potent (nanomolar) scaffolds present in the library. The three nanomolar scaffolds identified in our project are novel for IRAK1 and lend themselves for future development. HydraScreen outperforms traditional virtual screening methods in an unbiased prospective evaluation and offers advanced features such as ligand pose confidence scoring. Thus, SpectraView and HydraScreen are innovative tools which can aid and expedite early stages of drug discovery.