Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits

The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool ‘Emergent Self-Organizing Maps’ for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity

The anticancer and EGFR-TK/CDK-9 dual inhibitory potentials of new synthetic pyranopyrazole and pyrazolone derivatives: X-ray crystallography, in vitro, and in silico mechanistic investigations

Treatment options for the management of breast cancer are still inadequate. This inadequacy is attributed to the lack of effective targeted medications, often resulting in the recurrence of metastatic disorders. Cumulative evidence suggests that epidermal growth factor receptor (EGFR-TK) and cyclin-dependent kinases-9 (CDK-9) overexpression correlates with worse overall survival in breast cancer patients. Pyranopyrazole and pyrazolone are privileged options for the development of anticancer agents. Inspired by this proven scientific fact, we report here the synthesis of two new series of suggested anticancer molecules incorporating both heterocycles together with their characterization by IR, 1H NMR, 13C NMR, 13C NMR-DEPT, and X-ray diffraction methods. An attempt to get the pyranopyrazole-gold complexes was conducted but unexpectedly yielded benzylidene-2,4-dihydro-3H-pyrazol-3-one instead. This unexpected result was confirmed by X-ray crystallographic analysis. All newly synthesized compounds were assessed for their anti-proliferative activity against two different human breast cancer cells, and the obtained results were compared with the reference drug Staurosporine. The target compounds revealed variable cytotoxicity with IC50 at a low micromolar range with superior selectivity indices. Target enzyme EGFR-TK and CDK-9 assays showed that compounds 22 and 23 effectively inhibited both biological targets with IC50 values of 0.143 and 0.121 µM, respectively. Molecular docking experiments and molecular dynamics simulation were also conducted to further rationalize the in vitro obtained results