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Pharmacoinformatics strategies have been applied to explore promising selective estrogen receptor (ER) modulators (SERMs). A set of non-steroidal ligands was considered for both ERα and ERβ subtypes. Best pharmacophore models revealed with importance of hydrogen bond acceptor and hydrophobicity for both subtypes, along with an aromatic ring and hydrogen bond donor for α and β subtypes, respectively. Both models were validated, and further considered for virtual screening of National Cancer Institute database. Initial hits were sorted with a number of criteria, and finally the molecules have been proposed as promising SERMs. A molecular docking study explained that screened ligands formed a number of binding interactions with both ERs. The subtype receptors in complex with active and screened compounds were considered for molecular simulations to compare stability of the complexes. An analysis of binding energy found that screened ligands hold a strong affinity towards the selective receptor cavity. The proposed ligands might be promising leads for estrogen therapy after experimental validation tests.

Deposition of α–synuclein, tau and β–amyloid protein plaques in brain leads to neurodegeneration. A series of indolin derivatives, which can bind to α–synuclein and detect Parkinson's disease (PD), were used for development of QSAR and QAAR models. It is the first attempt of QSAR for any radiotracer agents used for detection of PD. The binding affinity against α–synuclein was used as dependent variable while independent variables, such as structural, topological, E-state keys, electronic, molecular shape analysis and spatial molecular descriptors were used for QSAR modeling. For QAAR modeling, the binding affinities of molecules for tau and β–amyloid along with different molecular descriptors were used as independent variables. All models were successfully developed using multiple linear regression method, and validated internally and externally, based on different standard criteria. This article describes how the derived models postulate that conformation of molecules and presence of unsaturated hydrocarbon chains, nitro, methoxy and amine functionalities play an important role in determining binding affinity.

Deposition of α–synuclein, tau and β–amyloid protein plaques in brain leads to neurodegeneration. A series of indolin derivatives, which can bind to α–synuclein and detect Parkinson's disease (PD), were used for development of QSAR and QAAR models. It is the first attempt of QSAR for any radiotracer agents used for detection of PD. The binding affinity against α–synuclein was used as dependent variable while independent variables, such as structural, topological, E-state keys, electronic, molecular shape analysis and spatial molecular descriptors were used for QSAR modeling. For QAAR modeling, the binding affinities of molecules for tau and β–amyloid along with different molecular descriptors were used as independent variables. All models were successfully developed using multiple linear regression method, and validated internally and externally, based on different standard criteria. This article describes how the derived models postulate that conformation of molecules and presence of unsaturated hydrocarbon chains, nitro, methoxy and amine functionalities play an important role in determining binding affinity.

Application of pesticides may have serious adverse consequences in environment. Birds are one of the most important non-target species that are harmed by agricultural chemical pesticides. In the current study, Monte Carlo optimization based Quantitative Structure Toxicity Relationship (QSTR) analyses were performed on a dataset containing diverse chemical pesticides with toxicity data determined on Bobwhite quail. Hybrid models containing SMILES and graph based descriptors were developed on three different training and test set combinations. The best model was selected based on validation statistics on internal training (n = 96) and external test (n = 31) as well as validation (n=25) sets. The best model was thoroughly analyzed to understand structural requirements of the chemical pesticides for higher avian toxicity. The models developed in the current analyses may be useful to design novel less toxic pesticides.

The present approach has aimed to investigate the physicochemical and structural requirements of a molecule to impart potential and selective PPARa agonistic activity using various ligand-based and structure-based molecular modeling techniques. These studies suggest the presence oxo heterocyclic ring connected with lipophilic substituents (phenyl, flurophenyl or any hydropbic alkyl chain) may responsible for hydrophobic interaction with the receptor. Both regression-based and classification-based QSAR, and HQSAR models infer that the ether linkage is very crucial for the activity, where as long alkyl chain or acyclic ring and the chloride substituent have detrimental effects. The pharmacophore models strongly depict the presence of two acceptors and hydrophobic region are very much essential for promising activity. The docking and MD studies suggest the presence of carboxyl group is essential for H-bond interaction with catalytic residues. The cheminformatics study provides key information on the structural pattern for imparting of potent and selective PPARa modulators.