We are proud to have satisfied customers advancing drug development worldwide.
“Our mission at Debiopharm is to develop tomorrow’s standard of care for cancer and improve patient quality of life. Accelerating the translation of our research to clinical success is a priority, and our work with VeriSIM Life is focused on this critical objective. With insights from the BIOiSIM platform, we believe our innovative therapies will rapidly move closer to addressing the unmet needs of cancer patients worldwide.”
“VeriSIM Life has an amazing capability to develop simple and robust algorithms quickly along with scientific interpretation behind data. Even in the case model building was challenging, their platform and approach were really helpful to achieve our goals through data curation, missing parameter optimization, mechanistic predictor setting and future/descriptor development by using their AI/ML technologies. I am confident that VeriSIM Life is an excellent partner on drug discovery and development.”
“I have been pleased working with VeriSIM Life. We have deployed models to simulate blood brain barrier permeability for the purpose of advancing our research into CNS disorder therapies. This allows us to screen investigative compounds more quickly. The team at VeriSIM Life is very knowledgeable of pharmacology, modeling and the application of machine learning. I am looking forward to deeper candidate development based on the in silico data generated to date.”
“Over the past several years, I have worked with a host of simulation and modeling softwares but the way VeriSIM Life’s, BIOiSIM, integrates machine learning with mechanistic modeling is very unique. Intelligent models with improved prediction capabilities can save a lot of resources and help bring better drugs to the market much faster than anticipated. I strongly believe that your platform has that convincing element to bring big players on board.”
“Verisimlife was already engaged in a project with my colleagues when I joined partway through in a technical oversight/review capacity. I was impressed with both the technical expertise of the team, their resourcefulness in devising approaches to deal with a limited and incomplete dataset, and their consideration of the scientific context of the project, beyond just the machine learning aspects. At a time when numerous smaller organizations are over-selling their AI efforts as cure-alls, I found VSL's down-to-earth but competent and insightful approach very refreshing. They also were excellent in their project management and communication/presentation skills, and I certainly felt they went above and beyond in responding to our questions and needs. I would not hesitate to recommend them for organizations looking for professional and knowledgeable external ML solutions.”
“We have worked with VeriSIM Life on several projects over the past 9 months and have been very pleased with our interactions and output. The team is not only very easy to work with but has also developed sophisticated models and analyses with the same level of passion and excitement as if we were all part of the same organization.”
“I personally like the artificial intelligence applied to drug discovery, and have seen many companies working in the space, you are serving a niche need which is drug development. Combination of physiological knowledge with mechanistic models can solve a lot of pressing issues pharma industry is facing right now such as unnecessary preclinical experiments, trial-and-error methodologies and translatability of current simulation solutions especially in the field of large molecules. I was skeptical though during my early conversations but gained confidence over time as I came to know more about your approach and framework.”
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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