摘要：Drug discovery is expensive and high-risk. Its main reasons of failure are lack of efficacy and toxicity of a drug candidate. Binding affinity for the biological target has been usually considered one of the most relevant figures of merit to judge a drug candidate along with bioavailability, selectivity and metabolic properties, which could depend on off-target interactions. Nevertheless, affinity does not always satisfactorily correlate with in vivo drug efficacy. It is indeed becoming increasingly evident that the time a drug spends in contact with its target (aka residence time) can be a more reliable figure of merit. Experimental kinetic measurements are operatively limited by the cost and the time needed to synthesize compounds to be tested, to express and purify the target, and to setup the assays. We present here a simple and efficient molecular-dynamics-based computational approach to prioritize compounds according to their residence time. We devised a multiple-replica scaled molecular dynamics protocol with suitably defined harmonic restraints to accelerate the unbinding events while preserving the native fold. Ligands are ranked according to the mean observed scaled unbinding time. The approach, trivially parallel and easily implementable, was validated against experimental information available on biological systems of pharmacological relevance.
