Virtual screening (VS) is a common task in the drug discovery process and is a computational method to identify promising compounds from a collection of hundreds to millions of possible compounds. What “promising” exactly means, depends on the context – it might be compounds that will likely exhibit certain pharmacological effects. Or compounds that are expected to non-toxic. Or combinations of these and other properties. Many methods are available for virtual screening including similarity, docking and predictive models.
So, given the plethora of methods which one do we use? There are many factors affecting choice of VS method including availability, price, computational cost and so on. But in the end, deciding which one is better than another depends on the use of benchmarks. There are two features of VS benchmarks: the metric employed to decide whether one method is better than another and the data used for benchmarking. This post focuses on the latter aspect.