A predictive machine learning model for virtual screening of potential inhibitors against Mycobacterium tuberculosis

A boosting-based ensemble machine learning model that utilizes molecular descriptors as input data has been developed to predict the values of binding free energy   of small-molecule compounds to the MmpL3 of Mycobacterium tuberculosis (Mtb), an essential mycolic acid and lipid transporter required for growth and cell viability. Testing this model on two large sets of structurally heterogeneous molecules via regression evaluation metrics MAE, MSE, R2, and R showed that it is comparable in predictive performance to the Vina scoring function of the QuickVina 2 molecular docking program, but allows one to significantly speed up the structure-based virtual screening, which is an important factor in the analysis of molecular libraries containing hundreds of thousands, and occasionally millions, of chemical structures. In this regard, the developed model can be used as an express method for the rapid selection of candidate compounds in chemical databases, followed by prediction of their poses in the MmpL3 binding site using molecular docking and a study of the stability of ligand/MmpL3 complexes via molecular dynamics methods. The results obtained demonstrate the high efficiency of the developed model and its significant potential for the use in virtual screening of candidate compounds with antibacterial action based on the inhibition of the MmpL3 protein of Mycobacterium tuberculosis, one of the priority therapeutic targets for the design of new effective therapeutics against drug-resistant tuberculosis.

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