Simulations Plus has announced strategic partnership programmes with three pharmaceutical businesses to advance AI-enabled modelling in drug development.
The partnerships aim to apply AI within scientifically validated modelling workflows and define scalable, next-generation approaches throughout the drug development life cycle.
The programmes will use Simulations Plus software platforms, including ADMET Predictor, GastroPlus, Thales, and MonolixSuite.
By working closely with these pharmaceutical companies, Simulations Plus intends to integrate AI into practical environments, informing workflow standardisation, product direction, and future commercial models.
Under these programmes, participating companies will embed Simulations Plus’ AI agents directly into model-informed drug development (MIDD) workflows.
This integration will support natural language interaction, automate data processing, coordinate simulations across several modelling engines, and generate interpretable results from complex pipelines.
Additionally, the initiatives are structured to establish broader enterprise adoption. Simulations Plus and its partners will work with information technology teams to define the deployment, governance, and integration of AI-enabled capabilities within existing systems.
This approach includes setting shared standards for transparency and reproducibility as AI becomes more deeply integrated into drug development processes.
Simulations Plus co-chief product and technology officer Jonathan Chauvin said: “Our approach to AI is grounded in how it operates within a complete system, not as a standalone capability.
“These collaborations will allow us to work alongside our partners, leveraging real-time scientific feedback and company data to continuously refine how workflows are orchestrated across our tools, ensuring AI-driven efficiencies translate into reproducible, traceable outcomes.”
Simulations Plus CEO Shawn O’Connor said: “Our customers are choosing to work with us because of the strength of our validated scientific engines and depth of our teams who apply them daily within real workflows, enabling us to translate AI into practical, deployable solutions.”
