AI-Driven Drug Discovery
for Pharma R&D

SDLabs helps tackle complex R&D optimization challenges by automating the DMTA (design-make-test-analyze) cycle. It recommends the most promising experiments,enabling faster iteration and more efficient identification of drug candidates, drastically shortening early discovery timelines.

Users can select from both  proprietary algorithms or open-source ML optimizers: Falcon (multi-objective global optimization), Emmental (constraint-guided optimization with Falcom), and SeMOpt (Bayesian optimization and transfer learning).

SDLabsoptimizes experiments, reduces manual trial-and-error, and guidesg scientists to the most promising candidates faster. Unlike traditional trial-and-error or DoE, AI-driven optimization intelligently navigates complex design spaces, minimizing experiments needed. Scientists can also leverage real-time, interactive data analytics to interpret results and make informed decisions without requiring coding or data science expertise.

SDLabs is used for experimental design, optimizing synthesis, screening, and process conditions, and analyzing multi-parameter datasets to accelerate early-stage drug discovery.

SDLabs models complex relationships in high-dimensional experimental spaces, including multiple categorical, continuous, and discrete variables with constraints. Scientists can optionally incorporate physicochemical descriptors to better explore and exploit the experimental design space, guiding the selection of the next experiment and converging to optimal conditions faster.

Bayesian optimization is a machine learning technique that balances exploration (testing new, uncertain conditions) and exploitation (refining known promising conditions) to recommend the next best experiments. This approach helps scientists maximize discovery while minimizing experimental effort. 

In SDLabs, our Falcon optimizer applies Bayesian optimization to efficiently navigate complex experimental spaces, while SeMOpt extends this capability through transfer learning, allowing knowledge gained from past experiments to accelerate new projects. Together, they help R&D teams reach better results faster and more reliably.