An important component of SmartZyme’s R&D approach is its robust set of algorithms.
These mathematical matrices import the vast scientific and
experimental data available in biomedical journals and databases to construct a
comprehensive decision making infrastructure.
The algorithms are then used to map the “hot spots” of the element of interest (an enzyme, for example) as well as guide the protein engineers in constructing the
Once mutated versions of the original element are created and screened, their
full spectrum of characteristic data is fed back into the algorithm to improve
and fine tune the decision making process.
Machine learning techniques go hand-in-hand with this
algorithmic mapping throughout the process, leading to enhancement of the computational
infrastructure for current and future engineering targets.