Computational models of neuronal biophysics and the characterization of potential neuropharmacological targets

The identification and characterization of potential pharmacological targets in neurology and psychiatry is a fundamental problem at the intersection between medicinal chemistry and the neurosciences. Exciting new techniques in proteomics and genomics have fostered rapid progress, opening numerous questions as to the functional consequences of ligand binding at the systems level. Psycho- and neuro-active drugs typically work in nerve cells by affecting one or more aspects of electrophysiological activity. Thus, an integrated understanding of neuropharmacological agents requires bridging the gap between their molecular mechanisms and the biophysical determinants of neuronal function. Computational neuroscience and bioinformatics can play a major role in this functional connection. Robust quantitative models exist describing all major active membrane properties under endogenous and exogenous chemical control. These include voltage-dependent ionic channels (sodium, potassium, calcium, etc.), synaptic receptor channels (e.g. glutamatergic, GABAergic, cholinergic), and G protein coupled signaling pathways (protein kinases, phosphatases, and other enzymatic cascades). This brief review of neuromolecular medicine from the computational perspective provides compelling examples of how simulations can elucidate, explain, and predict the effect of chemical agonists, antagonists, and modulators in the nervous system.