De-risk discovery & harness the power of AI to bring life-saving drugs to patients faster.
Drug discovery, research and development has reached a breaking point: bringing a new drug to patients costs far too much and takes far too long.
In the US, the FDA approves about 50 new drugs per year. And yet, 300 million patients suffering from unmet diseases have no direct treatment. The problem is that the process of translating potential drug candidates into safe and effective clinical trials is rife with inefficiency. The science involved in developing drugs is an incredibly complex process of experimentation. It is also very sequential, and so failures that appear later in the cycle often result in re-initiating earlier phases.
VeriSIM Life is changing this paradigm. Using hybrid AI technology, VeriSIM Life predicts if and where a drug is likely to hit a dead-end, long before researchers get there experimentally. BIOiSIM is a powerful drug development decision engine. With the help of hybrid AI technology, the system produces a “credit score” for a drug’s therapeutic potential.
Watch the full video to learn more, then reach out to our team to learn how we could help you de-risk your drug development process.
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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