Peer-reviewed with Merck and Boehringer Ingelheim in Biochemistry and Org. Process Res. Dev.
By the numbers:
fewer experiments than traditional DoE
faster than traditional DoE and HTE
prediction accuracy in guided campaigns
SuntheticsML learns from your data and points to the experiments that matter, so your team reaches its objectives in fewer rounds.
SuntheticsML models the interacting variables and high-dimensional spaces trial and error can't, with results that hold up in peer-reviewed research and scale from one scientist to enterprise teams.
Trusted by leading industrial pharma and chemical innovators, SuntheticsML platform is thoroughly validated with scientists to confidently deliver results.
Your team defines the objectives, Sunthetics ensures you achieve them faster.
No coding, ML, or data science expertise required. We work with your team to enable smooth adoption across your organization so scientists can focus on science.
Cutting edge models integrate chemical properties into your data for optimal analytical performance.
Navigate complex, noisy, or limited data while optimizing across multiple scientific goals.
SOC 2 and ISO 27001 certified. Your experimental data is encrypted, isolated, and never used to train anyone else's models.
Teams can use large existing data sets or start from scratch. It requires at least 5 data points to model and gets stronger as you iterate.
Traditional DoE limits the variables you can test and locks in experimental space before you have results. SuntheticsML allows you to test up to 20+ input and 20+ output variables across categorical, chemical, continuous, discrete, and time series. You define the goals and the model adapts after each iteration which performs better in complex scientific environments.
Getting started is straightforward. You upload your existing experimental data or begin with as few as 5 data points. The Sunthetics team works directly with your R&D team to configure the platform for your chemistry and goals. From there, the model guides your next experiments in real time.