Statistical thermodynamics relates the microscopic properties of atoms and molecules to macroscopic properties. This powerful relationship allows us to predict experimentally observable properties from atomistic simulations.

At Silicon Therapeutics, we use statistical thermodynamics to compute biologically relevant quantities such as binding affinity, selectivity, allostery, membrane permeability, and solubility from molecular dynamics simulations. The decomposition of energies into components and functional groups leads to insights that could not otherwise be gained. This process allows our scientists to reach a deeper understanding of protein targets and design new ligands that modulate properties in desirable ways.

Computing Meaningful Free Energies

Another way to think of statistical thermodynamics is to consider free energy as the determining factor in all biological processes. In this way, kinetics and thermodynamics allow us to avoid the abstraction of a simplified, empirically derived representation. This means we can compute the true free energy of a system and determine the subtle balance between entropy (the amount of disorder in a system) and enthalpy (the overall amount of energy in a system) without massive amounts of training data. We begin with first principle simulations and predict biologically meaningful properties. The compelling advantage of our approach at Silicon Therapeutics is that we can map our insights onto real-world protein targets and diseases, which we believe will lead to a higher probability of success in the development of new safe and effective treatments for patients.