Developing a quantitative understanding of the structure, function, and dynamics of protein-protein, protein-ligand, and protein-nucleic acid interactions, and how such interactions modulate protein function, is at the heart of a molecular-level understanding of disease and how to target mutant or otherwise pathogenic proteins with drugs that have a low side-effect profile.  Towards this end, computational strategies to predict high-specificity binding of drug ligands to proteins and strategies to simulate how the binding of such ligands modulates protein function are being developed, with a specific focus on targeted cancer therapy as a replacement for chemotherapy.  Specifically, machine learning methods using a range of physicochemical features will be applied to the development of highly accurate scoring functions for prediction of drug ligand binding sites.  After this stage, advanced sampling methods such as the Markov state model and features calculated therefrom, e.g. mutual information among protein residues and dynamical, structural, and topological features of metastable basins, or direct observation of a modulatory effect, will be used to assess allostery, that is, the ability of a binding event at one site to modulate the active site function.  Targeting allosteric sites holds the promise of greater specificity and a lower side effect profile compared with the standard approach of targeted cancer therapy involving inhibition of the active site.