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Using quantitative, data-driven approaches to make synthetic chemistry more predictable

The Center for Computer Aided Synthesis (C-CAS) uses quantitative, data-driven approaches to make synthetic chemistry more predictable. By reducing the time and resources needed to design and optimize synthetic routes, the tools and protocols developed in C-CAS help chemists to focus more on what molecules should be made and why rather than on how to make them.

Research Highlights

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Data Mining and Integration

Better data lead to better predictions. Current datasets used for reaction predictions are often incomplete.

Optimization and Machine Learning

Recent advances in using ML for retrosynthetic planning and forward synthesis prediction represent necessary but not sufficient tools for the realization of computer-assisted synthesis planning.

Molecule Synthesis Planning and Scoring

Synthesis pathways for the preparation of complex molecules are not unlike mazes, which are replete with unexpected twists and turns and dead ends.