Our company has reached a significant milestone in sustainability efforts, reducing carbon emissions by 30% over the past year. This achievement highlights our commitment to environmental responsibility and innovation in our industry.
Our company has reached a significant milestone in sustainability efforts, reducing carbon emissions by 30% over the past year. This achievement highlights our commitment to environmental responsibility and innovation in our industry.
The recent launch of our innovative product has captivated the market, receiving positive feedback from both consumers and industry experts. This product is set to redefine standards in our sector and enhance user experience.
The recent launch of our innovative product has captivated the market, receiving positive feedback from both consumers and industry experts. This product is set to redefine standards in our sector and enhance user experience.
In a bid to give back to the community, our company has partnered with a local charity to support underprivileged families. This initiative aims to provide essential resources and improve the quality of life for those in need.
In a bid to give back to the community, our company has partnered with a local charity to support underprivileged families. This initiative aims to provide essential resources and improve the quality of life for those in need.
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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