1. Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1
- Author
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Gintautas Kamuntavičius, Alvaro Prat, Tanya Paquet, Orestis Bastas, Hisham Abdel Aty, Qing Sun, Carsten B. Andersen, John Harman, Marc E. Siladi, Daniel R. Rines, Sarah J. L. Flatters, Roy Tal, and Povilas Norvaišas
- Subjects
Computational chemistry ,Artificial intelligence ,Machine learning ,Knowledge graph ,Deep learning ,Drug discovery ,Information technology ,T58.5-58.64 ,Chemistry ,QD1-999 - Abstract
Abstract Background Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model 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, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.
- Published
- 2024
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