SAN FRANCISCO, Oct. 1, 2024 /PRNewswire-PRWeb/ -- VeriSIM Life, a leader in AI-driven drug discovery and development, today announced its second collaboration agreement with Mayo Clinic, aimed at accelerating the development of new therapies for liver and metabolic diseases. The collaboration will leverage Mayo Clinic's extensive clinical and research expertise along with VeriSIM Life's pioneering AI-powered platform to address critical unmet medical needs.
This collaboration is designed to combine clinical insights with VeriSIM Life's cutting-edge "biology-first" AI platform. The focus will be on advancing the discovery and development of drugs targeting liver and metabolic diseases, where traditional drug development has often faced delays and high costs. By harnessing the predictive capabilities of VeriSIM Life's AI technology alongside Mayo Clinic's clinical expertise, the two organizations aim to deliver faster, more precise therapeutic solutions that address the root causes of these conditions.
"This partnership is a significant step forward in advancing the next generation of therapies, driven by a shared commitment to innovation, patient care, and addressing the unmet needs of those suffering from challenging conditions," said Dr. Jo Varshney, Founder and CEO of VeriSIM Life. "I am excited to extend what was already a fruitful collaboration with Mayo Clinic."
The need for faster, more effective drug development has never been more pressing. In 2024, the U.S. Food and Drug Administration (FDA) approved 22 new molecular entities (NMEs) and therapeutic biologics as of mid-July. Despite this progress, one-third of U.S. adults suffer from metabolic syndrome, placing a heavy burden on healthcare systems globally.
By utilizing VeriSIM Life's innovative AI-driven approach, which integrates biological, clinical, and chemical data, and Mayo Clinic's commitment to using advanced technologies like AI, this collaboration is set to push the boundaries of drug discovery and delivery.
This new agreement follows a successful collaboration initiated in 2022 between VeriSIM Life and Mayo Clinic, reinforcing both organizations' commitment to using cutting-edge technologies to improve patient outcomes.
About VeriSIM Life
VeriSIM Life has developed a sophisticated computational platform that leverages advanced AI and ML techniques to improve drug discovery and development by significantly reducing the time and money it takes to bring a drug to market. BIOiSIM® is a first-in-class 'virtual drug development engine' that offers unprecedented value for the drug development industry by narrowing down the number of drug compounds that offer anticipated value for the treatment or cure of specific illnesses or diseases. The platform predicts the likelihood of a candidate's success in clinical trials early in the preclinical stage, while reducing unnecessary experimentation and better informing key program decisions. For more information, visit www.verisimlife.com.
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
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