San Francisco, CA – September 8, 2025 – VeriSIM Life, a leading AI-first biotechnology company, today announced it has been named a finalist for the prestigious AI in Action award category in the 2025 Reuters Pharma Awards. This award recognizes organizations and individuals who have demonstrably worked to reduce patient burden, amplify patient communities, and advance the patient voice in pharmaceutical research and development.
VeriSIM Life was selected for its submission, Championing the Patient Cause: How VeriSIM Life Reduces Burden and Drives Equity Through Predictive AI and Digital Twins. The company’s proprietary BIOiSIM® platform creates highly predictive “digital twin” models that simulate drug effects in diverse virtual populations. These in silico models enable researchers to identify safety and efficacy signals earlier, design more inclusive trials, and accelerate the delivery of safe, effective, and equitable therapies to patients.
“Being named a finalist for the AI in Action award category is a tremendous honor for our team and an important validation of our mission,” said Dr. Jo Varshney, Founder and CEO of VeriSIM Life. “BIOiSIM was built with empathy, equity, and scientific rigor at its core. This recognition underscores how our technology not only improves the efficiency of drug development but also ensures patients—especially those historically overlooked—are represented and protected from the very beginning.”
BIOiSIM has already delivered measurable outcomes across multiple therapeutic areas. For example, the platform helped a pharmaceutical partner detect a liver toxicity risk before it reached patients, avoiding a costly and potentially harmful failed trial. In another case, VeriSIM Life’s virtual modeling accelerated a rare disease drug into clinical testing six months faster than industry norms, saving critical time for patients with no existing treatment options. Across programs, BIOiSIM reduces development timelines by an average of 2.5 years and saves over $3 million per asset while increasing the probability of clinical success.
By addressing systemic inefficiencies and inequities in R&D, VeriSIM Life is redefining the standard of patient-first innovation. Beyond reducing unnecessary animal and human testing, the company is championing inclusivity by enabling drug developers to model outcomes for pediatric, geriatric, rare disease, and racially diverse populations often excluded from clinical research.
“VeriSIM Life’s work happens behind the scenes, but its impact is deeply human,” added Dr. Varshney. “We are building a future where therapies reach patients faster, safer, and more affordably—and where no community is left behind in the process.”
The winners of the 2025 Reuters Pharma Awards will be announced later this year at the annual awards ceremony, which celebrates innovation and leadership shaping the future of global healthcare.
About VeriSIM Life
VeriSIM Life is an AI-first biotechnology company transforming drug development through predictive biosimulation. Its proprietary BIOiSIM® platform leverages advanced AI, mechanistic modeling, and virtual populations to help pharmaceutical and biotech companies make smarter, faster, and safer R&D decisions. By reducing reliance on animal testing, accelerating timelines, and promoting inclusivity in clinical trials, VeriSIM Life empowers its partners to deliver better medicines to patients worldwide.
For more information, visit www.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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