In the mission to address unmet clinical needs, the future scope of druggable targets will be much wider than it is today. Physics-based simulation and modeling is the key to unlock uncharted therapeutic territories.
For many years, structure-based drug discovery has treated proteins as rigid entities, relying heavily on a small number of static crystal structures. We use long-time-scale, atomic-level simulation to characterize the biophysical dynamics, kinetics, and druggability of new and challenging protein targets.
Synthesizable chemical space is enormous. Powered by molecular dynamics, machine learning, and free energy calculations, we search multi-billion molecule libraries to achieve precise control over the potency and selectivity of new drug candidates. We always develop a diverse set of chemical solutions in order to overcome later-stage DMPK liabilities.
Through large-scale automation of our platform, we are building a proteome-wide database of long-time-scale molecular dynamics simulations. Our aim is to accelerate new target drug discovery against many diverse classes of previously undrugged proteins. Conducting simulations and analyses across entire protein families provides a data-independent approach for rational poly-pharmacology and to mitigate off-target toxicity.