Insilico Medicine (“Insilico”), a clinical stage, end-to-end artificial intelligence (AI)-driven drug discovery company, held the launch ceremony for Life Star – a sixth generation Intelligent Robotics Drug Discovery Laboratory – in Suzhou BioBAY Industrial Park on Dec. 29. The fully automated AI-powered robotics laboratory performs goal discovery, compound screening, precision medicine development, and translational research. The lab will allow Insilico to further speed up its end-to-end drug discovery and optimize the success rate of its drug development because it moves its novel therapeutics through clinical trials.
High-throughput screening, automatic modules, and machine learning data generation will not be recent within the biomedical sector. So far, fifth generation robotics laboratories have realized full automation with no human bias or influence, connected multiple processes, and generated high-quality data that may be used for machine learning. Insilico Medicine’s lab takes this process one step further to include AI in decision-making and deeply integrate AI with automation, robotics, and biological capabilities to enable a recent generation of intelligent robotic labs.
We hope to usher in a recent era of biology labs. Insilico’s robotics lab has an AI brain, an automatic machine body, and the limbs of varied complex robots. The AI brain has been trained and verified through Insilico projects and has learned from years of experience collaborating with global pharmaceutical corporations. It will possibly perform systematic learning based on the knowledge provided, and assist within the decision-making of goal discovery and identification. Within the Intelligent Robotics Lab, the AI brain will propose potential targets and design automated experiments and workflows based on experimental results.”
Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine
The Intelligent Robotics Lab forms a closed loop by combining Pharma.AI (Insilico Medicine’s end-to-end AI platform for goal discovery, novel drug design, and clinical trial prediction) with fully automated biological experiment functional modules.
Specifically, after the Pharma.AI goal discovery platform PandaOmics predicts novel targets for specific diseases, the robotics lab will conduct early-stage drug discovery experiments like goal validation, high-throughput hit compound screening, hit-to-lead optimization, lead-to-preclinical candidate (PCC) confirmation, and translational research. These biological experiments which can be traditionally conducted by humans will probably be achieved through six fully automated modules including sample management and quality control, compound management, automated cell culture, high-throughput screening, high-content cell imaging, and next-generation sequencing. All of the high-quality data generated by the robotics lab will complement and expand Insilico’s existing data resources to coach and optimize the AI platform, further strengthening Insilico’s biological data factory.
Feng Ren, PhD, Chief Scientific Officer and co-CEO of Insilico Medicine, said, “Goal identification is the source of recent drug development. Considered one of the key challenges currently faced by the biomedical industry is finding the precise goal. Now, Insilico Medicine proposes a comprehensive solution through the Intelligent Robotics Lab. First, the lab helps to discover and confirm potential targets efficiently. It then evaluates and analyzes the activity and druggability of the compounds designed. As well as, it helps discover biomarkers and conduct studies on the mechanism of motion during preclinical and translational research. The Robotics Lab has begun working on Insilico projects and we stay up for providing this tool to advance the research of our academic and business partners.”
Founded in 2014, Insilico Medicine is considered one of the primary players in AI-driven drug discovery. It has built a strong and efficient end-to-end AI platform, Pharma.AI, to enhance the speed, cost, and efficiency of drug discovery. Since 2021, Insilico has advanced 9 preclinical candidates, including drug candidates with novel targets and structures discovered by AI, and drug candidates with desirable properties designed by AI aiming at known targets. Projects are widely spread across multiple disease areas including fibrosis, immuno-oncology, inflammation, and COVID-19.