With a world-class team producing world-changing solutions, we define our values in ways unique to the highly collaborative and interdisciplinary approach we’re taking to develop our products and ensure that every action we take moves our mission forward.
We help each other flourish, and strive to create an environment fostering the personal and professional growth of our entire team.
We speak the truth and are transparent on our methods and scientific thinking internally and externally to reduce the known “black box” with ML/AI.
We are a team of highly motivated group who are driven to get medicines to patients faster, cheaper, safer and minimize animal suffering.
We empathize that there are people and animals who are impacted by the inconsistent success rate of preclinical to clinical testing while also empathizing with our peers and helping one another in all ways possible.
We believe in solving the translatability problem using first principles and by engaging and integrating known knowledge with futurist, deep technologies (AI/ML). We value growth mindset and understand the “need” for steep learning curve across disciplines to drive such innovation.
Please send your resume and cover letter indicating interest to info@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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