The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the “golden age for GPCR structural biology.” Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand– and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Mycobacterium tuberculosis (Mtb) the main causative agent of tuberculosis, is the main reason why this disease continues to be a global public health threat. It is therefore imperative to find a novel antitubercular drug target that is unique to the structural machinery or is essential to the growth and survival of the bacterium. One such target is the enzyme l,d-transpeptidase 2, also known as LdtMt2, a protein primarily responsible for the catalysis of 3→3 cross-linkages that make up the mycolyl–arabinogalactan–peptidoglycan complex of Mtb. In this study, structure-based pharmacophore screening, molecular docking, and in silico toxicity evaluations were employed in screening compounds from a database of synthetic compounds. Out of the 4.5 million database compounds, 18 structures were identified as high-scoring, high-binding hits with very satisfactory absorption, distribution, metabolism, excretion, and toxicity properties. Two out of the 18 compounds were further subjected to in vitro bioactivity assays, with one exhibiting a good inhibitory activity against the Mtb H37Ra strain.

Computer-aided drug discovery and development approaches such as virtual screening, molecular docking, and in silico drug property calculations have been utilized in this effort to discover new lead compounds against tuberculosis. The enzyme 7,8-diaminopelargonic acid aminotransferase (BioA) in Mycobacterium tuberculosis (Mtb), primarily involved in the lipid biosynthesis pathway, was chosen as the drug target due to the fact that humans are not capable of synthesizing biotin endogenously. The computational screening of 4.5 million compounds from the Enamine REAL database has ultimately yielded 45 high-scoring, high-affinity compounds with desirable in silico absorption, distribution, metabolism, excretion, and toxicity properties. Seventeen of the 45 compounds were subjected to bioactivity validation using the resazurin microtiter assay. Among the 4 actives, compound 7 ((Z)-N-(2-isopropoxyphenyl)-2-oxo-2-((3-(trifluoromethyl)cyclohexyl)amino)acetimidic acid) displayed inhibitory activity up to 83% at 10 μg/mL concentration against the growth of the Mtb H37Ra strain.

Curcumin is a yellow-colored ingredient in dietary spice turmeric (Curcuma longa Linn). This nontoxic polyphenol has antitumor, anti-inflammatory, apoptotic, and antioxidant activities. The ingested curcumin is reduced to multihydrated forms with more potent therapeutic potentials by the curcumin reductase (CurA) from commensal Escherichia coli. In this study, we demonstrated that Vibrio vulnificus CurA (VvCurA) with 87% sequence similarity to the E. coli CurA exhibits the curcumin-reducing activity through spectrophotometric detection of NADPH oxidation and high performance liquid chromatographic analysis of curcumin consumption and product generation. Afterward, we determined the crystal structures of VvCurA and the VvCurA/NADPH complex, and made the in silico model of the VvCurA/NADPH/curcumin ternary complex through induced fit docking. Based on structural information, active site residues that play critical roles in catalysis have been identified and characterized by mutational and kinetic studies, leading us to propose the reaction mechanism of CurA. KEYWORDS: curcumin-reducing enzyme, crystal structure of apo VvCurA, crystal structure of the VvCurA/NADPH complex, in silico model of the VvCurA/NADPH/curcumin complex, enzyme mechanism

The viral infection caused by the dengue virus (DENV) is one of the most challenging diseases in the tropical regions of the world. The absence of drugs for dengue to this date calls for intense efforts to discover and develop the much coveted therapeutics for this mosquito-borne disease. One of the most attractive antiviral targets is the DENV RNAdependent RNA polymerase (RdRp), which catalyzes the de novo initiation as well as elongation of the flavivirus RNA genome. In this work, almost 5000 natural products were docked to DENV RdRp. The top 197 molecules with greater binding energies than the known ligand of the target were further clustered down to furnish 35 classes of molecular structures. These compounds with satisfactory predicted drug properties and with known natural origin can be further explored to pave the way for the first anti-dengue drug.