Identification of InhA-Inhibitors Interaction Fingerprints that Affect Residence Time

Drug development is a complex process that remains subject to risks and uncertainties. In its early days, much emphasis was placed on the equilibrium binding affinity of a drug to a particular target, which is described by the equilibrium dissociation constant (\(K_{d}\)). However, there are a large number of drugs that exhibit non-equilibrium binding properties. For this reason, optimization of other kinetic parameters such as dissociation constants (\(k_{off}\)) and association constants (\(k_{on}\)) is becoming increasingly important to improve accuracy in measuring in vivo efficacy. To achieve this, the concept of residence time between drug and target (\(\tau \)) was developed to account for the continuous elimination of the drug, the absence of equilibrium conditions, and the conformational dynamics of the target molecules. Residence time has been shown to be a better estimate of drug lifetime potency than equilibrium binding affinity and is recognized as a key parameter in drug development. However, because residence time is only one measure of drug potency, it provides only a limited picture of binding kinetics and affinity.

A machine-learning algorithm was proposed to identify molecular features affecting protein-ligand binding kinetics for a set of similar compounds. Molecular dynamics simulations of \(\tau \)RAMD results were used as model input. The study confirmed that \(\tau \)RAMD provides information about the characteristics of the dissociation pathway since the obtained dissociation trajectories can be used to identify the interactions that occur and the conformational changes of the system at subsequent time points. The proposed algorithm made it possible to obtain information on protein-ligand contacts that are specific to their residence times.