Chemical Space Exploration of DprE1 Inhibitors Using Chemoinformatics and Artificial Intelligence

Tuberculosis (TB), entrained by Mycobacterium tuberculosis, continues to be an enfeebling disease, killing nearly 1.5 million people in 2019, with 2 billion people worldwide affected by latent TB. The multidrug-resistant and totally drug-resistant emerging strains further exacerbate the TB infection. The cell wall of bacteria provides critical virulence components such as cell surface proteins, regulators, signal transduction proteins, and toxins. The cell wall biosynthesis pathway of Mycobacterium tuberculosis is exhaustively studied to discover novel drug targets. Decaprenylphosphoryl-β-d-ribose-2'-epimerase (DprE1) is an important enzyme involved in the arabinogalactan biosynthetic pathway of Mycobacterium tuberculosis cell wall and is essential for both latent and persistent bacterial infection. We analyzed all known ∼1300 DprE1 inhibitors to gain deep insights into the chemogenomic space of DprE1-ligand complexes. Physicochemical descriptors of the DprE1 inhibitors showed a marked lipophilic character forming a cluster distinct from the existing TB drugs, as revealed by the principal component analysis. Similarity analysis using Murcko scaffolds and rubber band scaling revealed scarce representation of the chemical space. Further, Murcko scaffold analysis uncovered favorable and unfavorable scaffolds, where benzo and pyridine-based core scaffolds exhibit the highest biological activity, as evidenced by their MIC and IC₅₀ values. Automatic SAR and R-group decomposition analysis resulted in the identification of substructures responsible for the inhibitory activity of the DprE1 enzyme. Further, with activity cliff analysis, we observed prominent discontinuity in the SAR of DprE1 inhibitors, where even simple structural modification in the chemical scaffold resulted in significant potency difference, presumably due to the binding orientation and interaction in the active site. Thiophene, 6-membered aromatic rings, and unsubstituted benzene ring-based toxicophores were identified in the DprE1 chemical space using an artificial intelligence approach based on inductive logic programming. This paper, hence, ushers in new insights for the design and development of potent covalent and non-covalent DprE1 inhibitors and guides hit and lead optimization for the development of non-hazardous small molecule therapeutics for Mycobacterium tuberculosis.