Piritrexim

Oral Cancer is one of the major killers in India and of global concern.An attempt has been made here to identify small mol. inhibitors of Bcl-2 (B-cell lymphoma 2) protein through docking studies in search of new oral cancer drugs from a curated set of 276 known anti-neoplastic agents obtained from PubChem.The Bcl-2 protein (PDB id:6QGH), being complexed with ligand ABT-263, has been detached from the complex.The ligand-free protein and 276 compounds have been prepared for docking studies using corresponding tools available in Discovery Studio (DS), ver.4.1.The 276 compounds have been docked on the first site of the protein using LibDock docking program available in DS.By considering Methotrexate as the reference compound (LibDock score: 114.76), we have identified 25 compounds as the most potential oral cancer drugs having LibDock scores greater than 100.Another set of 114 compounds with LibDock scores between 80 and 100 have also been identified as the next set of potential drug candidates and some of them show anti-apoptosis properties.Two other compounds with LibDock scores below 80 have been identified for comparative analyses with some high-scoring compoundsThe docking results have been given in tables and pictures (screenshots) of the docked poses of selected compounds have been shown to illustrate the findings.Finally, the most suitable potential compounds have been identified by applying Lipinski′s Rule of 5.The present approach using known anti-neoplastic agents is believed to help discover potential anti-oral cancer drug candidates.

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.