We are proud to have satisfied customers advancing drug development worldwide.

“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.”

- DMPK Leader at Pfizer
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“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.”

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“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.”

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“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.”

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