2-Arylthio-5-iodo pyrimidine derivatives firstly proved to be effective against HBV, which paves the way for future development of nonnucleoside anti-HBV agents.
Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single‐structure docking runs, ensemble docking and a similarity‐based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure‐based virtual screening of malleable proteins, including kinases, some viral enzymes and anti‐targets.
HIV-1 reverse transcriptase (RT) is one of the most important enzymes required for viral replication, thus acting as an attractive target for antiretroviral therapy. Pyrimidine analogues reportedly have selective inhibition on HIV-1 RT with favorable antiviral activities in our previous study. To further explore the relationship between inhibitory activity and pharmacophoric characteristics, field-based QSAR models were generated and validated using Schrodinger Suite (correlation coefficient of .8078, cross-validated value of 0.5397 for training set and Q of 0.4669, Pearson's r of .7357 for test set). Docking, pocket surfaces, and pharmacophore study were also investigated to define the binding pattern and pharmacophoric features, including (i) π-π interaction with residue Tyr181, Tyr188, and Trp229 and p-π interaction with His235 and (ii) hydrogen bond with residue Lys101 and halogen bond with residue Tyr188. The pharmacophore features of six-point hypothesis AADRRR.184, AAADRR.38, and AADRRR.26 further complimented to the docking and QSAR results. We also found that the protein-ligand complex exhibited high relative binding free energy. These observations could be potentially utilized to guide the rational design and optimization of novel HIV-1 RT inhibitors.
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