Our CEO recently participated in a panel discussion where they shared insights on future trends in the industry. The conversation focused on technological advancements and their impact on business strategies.
Our CEO recently participated in a panel discussion where they shared insights on future trends in the industry. The conversation focused on technological advancements and their impact on business strategies.
We are proud to announce that our company has received an award for excellence in customer service. This recognition reflects our dedication to providing outstanding support and satisfaction to our clients.
We are proud to announce that our company has received an award for excellence in customer service. This recognition reflects our dedication to providing outstanding support and satisfaction to our clients.
Our company is excited to announce its expansion into new markets, aiming to reach a broader audience and enhance our global presence. This strategic move is expected to drive growth and innovation.
Our company is excited to announce its expansion into new markets, aiming to reach a broader audience and enhance our global presence. This strategic move is expected to drive growth and innovation.
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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