Pharmacophore modeling, 3D-QSAR, and MD simulation-based overture for the discovery of new potential HDAC1 inhibitors.

Histone deacetylases (HDACs) are important epigenetic regulators that modulate the activity of histone and non-histone proteins leading to various cancers. Histone deacetylase 1 (HDAC1) is a member of class 1 HDAC family related to different cancers. However, the nonselective profile of existing HDAC1 inhibitors restricted their clinical utility.

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches.

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders.

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors.

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features.

Fragment-based structural exploration and chemico-biological interaction study of HDAC3 inhibitors through non-linear pattern recognition, chemical space, and binding mode of interaction analysis.

HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, -alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition.

Fragment-based investigation of thiourea derivatives as VEGFR-2 inhibitors: a cross-validated approach of ligand-based and structure-based molecular modeling studies.

Angiogenesis is mediated by the vascular endothelial growth factor (VEGF) that plays a key role in the modulation of progression, invasion and metastasis, related to solid tumors and hematological malignancies. Several small-molecule VEGFR-2 inhibitors are marketed, but their usage is restricted to specific cancers due to severe toxicities. Therefore, cost-effective novel small molecule VEGFR-2 inhibitors may be an alternative to overcome these adverse effects.

Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening.

Type 2 diabetes mellitus is a metabolic disorder that requires potent therapeutic approaches. The FAM3B is a cytokine-like protein also referred to as PANcreatic-DERrived factor (PANDER) which mainly exists in pancreatic islets. In the process of identifying potential inhibitors with the aid of structure-based method PANDER protein is identified as a novel therapeutic target against type 2 diabetes mellitus as it involved in the development of type 2 diabetes by negatively regulating the pancreatic β-cell function and insulin sensitivity in the liver.

Identification of potential Aurora kinase-C protein inhibitors: an amalgamation of energy minimization, virtual screening, prime MMGBSA and AutoDock.

Genomic instability is a trademark of cancer evolution, there is still a wide necessity to discover safe and effective anti-cancer drugs. Aurora kinase-C protein is a member of the Aurora kinase family involved in regulating mitosis phases of the cell cycle. Aurora kinase-C protein has an aberrant expression at spindle assembly checkpoint leading to human cervical cancer and colorectal cancer.