Cyclothialidines are a class of bacterial DNA gyrase B (GyrB) subunit inhibitors, targeting its ATP-binding site. Starting from the available structural information on cyclothialidine GR122222X (2), an in silico virtual screening campaign was designed combining molecular docking calculations with three-dimensional structure-based pharmacophore information. A novel class of 2-amino-4-(2,4-dihydroxyphenyl)thiazole based inhibitors (5–9) with low micromolar antigyrase activity was discovered.
COBISS.SI-ID: 2731377
Quantitative structure–activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Using the non-linear technique CP-ANN, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure–activity relationship of fluoroquinolones.
COBISS.SI-ID: 4355354
A classical virtual combinatorial chemistry approach (CombiChem) was applied for combinatorial generation of 5590 novel structurally-similar 6-fluoroquinolone analogs by using a virtual synthetic pathway with selected primary (43) and secondary amines (130).Quantitative structure-activity relationships (QSAR) study on selected 304 (filtered by drug-likeness properties) virtually generated 6-fluoroquinolone analogs with unknown activity values was performed using a pre-built five-parameter multiple linear regression (MLR) model (Rtr = 0.8417, Rtr-cv = 0.7884).
COBISS.SI-ID: 4501786
The review is focused on the use of computational chemistry in the design and screening of novel lead compounds, concentrating in particular on the work of Slovenian researchers
COBISS.SI-ID: 4488218
CP-ANN technique was used to build 54 different QSAR models. The models were built for three sets of fluoroquinolones considering their antituberculosis activity and using different technical parameters (dimension of network and number of learning epochs). The models served as a reliable basis for ranking by a new powerful method based on sum of ranking differences (SRD). With the applied SRD procedure we can find the optimal ones. The best model can be selected easily for the first set. Two models can be recommended for the second set, and no recommended model was found for the set3.
COBISS.SI-ID: 4510490