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Raphael ThysInsilico Medicine ("Insilico"), a clinical stage, end-to-end artificial intelligence (AI)-driven drug discovery company, held the launch ceremony for Life Star – a 6th generation Intelligent Robotics Drug Discovery Laboratory – in Suzhou BioBAY Industrial Park on Dec. 29. The fully automated AI-powered robotics laboratory performs target discovery, compound screening, precision medicine development, and translational research. The lab will allow Insilico to further accelerate its end-to-end drug discovery and optimize the success rate of its drug development as it moves its novel therapeutics through clinical trials.
High-throughput screening, automatic modules, and machine learning data generation are not new in the biomedical sector. To date, 5th generation robotics laboratories have realized full automation with no human bias or influence, connected multiple processes, and generated high-quality data that can be used for machine learning. Insilico Medicine's lab takes this process one step further to incorporate AI in decision-making and deeply integrate AI with automation, robotics, and biological capabilities to enable a new generation of intelligent robotic labs.
We hope to usher in a new era of biology labs. Insilico's robotics lab has an AI brain, an automated machine body, and the limbs of various complex robots. The AI brain has been trained and verified through Insilico projects and has learned from years of experience collaborating with global pharmaceutical companies. It can carry out systematic learning based on the information provided, and assist in the decision-making of target discovery and identification. In 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 target discovery, novel drug design, and clinical trial prediction) with fully automated biological experiment functional modules.
Specifically, after the Pharma.AI target discovery platform PandaOmics predicts novel targets for specific diseases, the robotics lab will conduct early-stage drug discovery experiments like target validation, high-throughput hit compound screening, hit-to-lead optimization, lead-to-preclinical candidate (PCC) confirmation, and translational research. These biological experiments that are traditionally conducted by humans will 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 the high-quality data generated by the robotics lab will complement and expand Insilico's existing data resources to train and optimize the AI platform, further strengthening Insilico's biological data factory.
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Feng Ren, PhD, Chief Scientific Officer and co-CEO of Insilico Medicine, said, "Target identification is the source of new drug development. One of the major challenges currently faced by the biomedical industry is finding the right target. Now, Insilico Medicine proposes a comprehensive solution through the Intelligent Robotics Lab. First, the lab helps to identify and verify potential targets efficiently. It then evaluates and analyzes the activity and druggability of the compounds designed. In addition, it helps discover biomarkers and conduct studies on the mechanism of action during preclinical and translational research. The Robotics Lab has begun working on Insilico projects and we look forward to providing this tool to advance the research of our academic and business partners."
Founded in 2014, Insilico Medicine is one of the first players in AI-driven drug discovery. It has built a powerful and efficient end-to-end AI platform, Pharma.AI, to improve 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.
Tags: Aging, Artificial Intelligence, Automation, Brain, Cancer, Cell, Cell Culture, Cell Imaging, Central Nervous System, Clinical Trial, Compound, covid-19, Drug Discovery, Drugs, Fibrosis, High-throughput screening, Imaging, immunity, Immuno-oncology, Inflammation, Laboratory, Machine Learning, Medicine, Nervous System, Oncology, Precision Medicine, Preclinical, Research, Sample Management, Therapeutics