The web-based platform aims to accelerate pharmaceutical research
AI solution developer SynSilico has launched the pilot version of its web-based optimisation platform INNOptimizer.
The platform is designed to reduce the number of experiments needed in the development and optimisation of processes, protocols, formulations and compositions in pharmaceutical research. It can support processes such as drug formulation optimisation, bioprocess design, and QbD-based protocol refinement.
INNOptimizer uses Bayesian Optimisation, a method for enhancing expensive, black-box functions using a probabilistic model. This method works well for resource-intensive experiments, particularly scientific research and pharmaceutical development.
Advanced analytics and visualisation help INNOptimizer to model the behaviour of systems and continuously learn from the results. Learning by design minimises the number of iterations required for users to achieve their goals.
Users can configure optimisation scenarios to fit their needs, including adjustments for manufacturing protocol, formula optimisation and modifying chemical reactions.
The platform employs an algorithm-driven framework that supports multivariate experimental design, sequential learning with uncertainty quantification, data-efficient exploration and exploitation and support for black-box systems.
While suitable for the pharmaceutical industry, INNOptimizer also assists numerous industries, including chemical manufacturing, materials science and food technology.
