San Francisco, CA — December 3, 2025 — VeriSIM Life, a leading innovator in AI-driven biosimulation and translational intelligence, today announced that it has been named the winner of the AI in Action Award at the 2025 Reuters Global Pharma Awards, an international program recognizing the most impactful advances shaping the future of the life sciences industry.
The AI in Action Award celebrates organizations that are applying artificial intelligence to solve high-stakes, real-world challenges with measurable scientific and operational impact. VeriSIM Life was selected for its BIOiSIM® platform, a mechanistically grounded, explainable AI system that simulates human biology to predict safety, efficacy, dose-exposure, and translational outcomes far earlier in development. By generating human-relevant insights long before traditional in vivo studies, BIOiSIM improves confidence in decision-making, reduces reliance on animal testing, and accelerates the path to first-in-human studies.
VeriSIM Life was chosen from a distinguished group of finalists, including Takeda, Pfizer, Amgen, AstraZeneca, and Sanofi, underscoring the significance of its contribution to predictive, computationally driven drug development.
“Being recognized with the Reuters Global Pharma AI in Action Award is an extraordinary honor,” said Dr. Varshney, Founder and CEO of VeriSIM Life. “This award validates our mission to bring explainable, human-relevant AI to the forefront of drug development so that safer, more effective therapies reach patients faster. We share this recognition with our team, partners, and clients who are advancing a new standard for translational science.”
The Reuters Global Pharma Awards spotlight achievements that drive meaningful progress across technology, patient outcomes, sustainability, and operational excellence worldwide. Winners are selected by an independent panel of industry experts, analysts, and scientific leaders.
This recognition further strengthens VeriSIM Life’s position as a trusted partner for pharmaceutical and biotechnology organizations seeking to modernize R&D with predictive, mechanistic, and human-centric simulation. BIOiSIM continues to play a critical role in de-risking development, improving translational certainty, and supporting regulatory-ready evidence packages across therapeutic areas.
VeriSIM Life is transforming early drug development through its AI-enabled biosimulation platform, BIOiSIM®, which integrates mechanistic modeling, machine learning, and diverse biological datasets to predict how medicines will behave in the human body. The platform improves clinical success rates, reduces unnecessary testing, streamlines translational strategy, and accelerates the path from discovery to clinic.
The Reuters Global Pharma Awards recognize excellence across the global pharmaceutical industry, honoring organizations that drive innovation in science, technology, operational performance, and patient outcomes.
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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