From target binding to neuropharmacological effects: what should count as a credible computational forecast?

One of the central problems in computational neuropharmacology is deciding what we actually mean when we say that a model can “predict” or “forecast” the effects of a compound on the brain. Target-binding data are an obvious starting point, but they are rarely the endpoint.

Two compounds acting at the same nominal receptor, for example, can produce substantially different biological outcomes because affinity is only one variable in a much larger system: intrinsic efficacy and functional selectivity, receptor and transporter distribution, cell type, downstream signaling, pharmacokinetics, off-target activity, network state, and interactions with other signaling systems can all affect the eventual phenotype. The aim of this project is to provide the tools that can integrate into new and existing drug discovery pipelines to help researchers converge on a set of multiscale standards for determining what qualifies as a comprehensive computational forecast.