Herein, we described a highly regio- and enantioselective Friedel–Crafts alkylation of aniline derivatives with in situ generated ortho-quinone methides enabled by chiral phosphoric acid, furnishing a wide range of enantioenriched triarylmethanes bearing three similar benzene rings in high yields (up to 98%) with excellent stereoselectivities (up to 98% ee). Furthermore, the large-scale reactions and diversified transformations of product demonstrate the practicality of the protocol. Density functional theory calculations elucidate the origin of the enantioselectivity.

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q2 = 0.636, r2ncv = 0.988, r2pred = 0.658; CoMSIA: q2 = 0.843, r2ncv = 0.989, r2pred = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.

In the recent cancer treatment, B-Raf kinase is one of key targets. Nowadays, a group of imidazopyridines as B-Raf kinase inhibitors have been reported. In order to investigate the interaction between this group of inhibitors and B-Raf kinase, molecular docking, molecular dynamic (MD) simulation and binding free energy (ΔGbind) calculation were performed in this work. Molecular docking was carried out to identify the key residues in the binding site, and MD simulations were performed to determine the detail binding mode. The results obtained from MD simulation reveal that the binding site is stable during the MD simulations, and some hydrogen bonds (H-bonds) in MD simulations are different from H-bonds in the docking mode. Based on the obtained MD trajectories, ΔGbind was computed by using Molecular Mechanics Generalized Born Surface Area (MM-GBSA), and the obtained energies are consistent with the activities. An energetic analysis reveals that both electrostatic and van der Waals contributions are important to ΔGbind, and the unfavorable polar solvation contribution results in the instability of the inhibitor with the lowest activity. These results are expected to understand the binding between B-Raf and imidazopyridines and provide some useful information to design potential B-Raf inhibitors.

Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent non-steroidal aromatase inhibitors (NSAIs) with lower side effects and overcome cellular resistance, Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database was used to derive 3D pharmacophore models. The obtained best pharmacophore model contains one acceptor atom, one donor atom, and two hydrophobes, which was used in effective alignment of dataset. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 84 structurally diverse NSAIs to build 3D-QSAR models based on both pharmacophore and docking alignments. The CoMFA and CoMSIA models based on the pharmacophore alignment show better statistical results (CoMFA: q 2 = 0.634, r ncv 2 = 0.986, r pred 2 = 0.737; CoMSIA: q 2 = 0.668, r ncv 2 = 0.926, r pred 2 = 0.708). This 3D-QSAR approach provides significant insights that can be used to develop novel and potent NSAIs. In addition, the best pharmacophore model was used as a 3D query for virtual screening against NCI2000 database. The hit compounds were further filtered by docking, and their biological activities were predicted by the CoMFA and CoMSIA models, and six structurally diverse compounds with good predicted pIC 50 values were obtained, which are expected to design novel NSAIs with new skeletons.