VeriSIM to share NIH Small Business Innovation Research (SBIR) grant to accelerate AI-driven drug discovery for substance use disorder
VeriSIM to share NIH Small Business Innovation Research (SBIR) grant to accelerate AI-driven drug discovery for substance use disorder
Financing led by Morpheus Ventures supports further revenue growth for the company’s next-generation AI platform, redefining the preclinical translational drug development landscape by accurately predicting and accelerating clinical outcomes January 6, 2022–SAN FRANCISCO–(BUSINESS WIRE)–VeriSIM Life (VeriSIM) www.verisimlife.com, the leading artificial intelligence (AI)-enabled, unique…
Financing led by Morpheus Ventures supports further revenue growth for the company’s next-generation AI platform, redefining the preclinical translational drug development landscape by accurately predicting and accelerating clinical outcomes January 6, 2022–SAN FRANCISCO–(BUSINESS WIRE)–VeriSIM Life (VeriSIM) www.verisimlife.com, the leading artificial intelligence (AI)-enabled, unique…
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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