@article { ISI:000299158700036,
	title = {Tuberculosis drug streptomycin as a potential cancer therapeutic: inhibition of miR-21 function by directly targeting its precursor},
	journal = {Angewandte Chemie-International Edition},
	volume = {51},
	number = {4},
	year = {2012},
	month = {DEC},
	pages = {1019-1023},
	publisher = {WILEY-BLACKWELL},
	type = {Article},
	address = {COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA},
	keywords = {Apoptosis, drug discovery, Gene expression, inhibitors, RNA},
	issn = {1433-7851},
	doi = {10.1002/anie.201106455},
	author = {Bose, Debojit and Jayaraj, Gopal Gunanathan and Suryawanshi, Hemant and Agarwala, Prachi and Pore, Subrata Kumar and Banerjee, Rajkumar and Maiti, Souvik}
}
@article {47329,
	title = {Pharmacore maping based on docking, ADME/toxicity, virtual screening on 3,5-dimethyl-1,3,4-hexanetriol and dodecanoic acid derivates for anticancer inhibitors},
	journal = {Journal of Biomolecular Structure \& Dynamics},
	volume = {39},
	year = {2021},
	month = {JAN},
	pages = {4490-4500},
	type = {Article},
	abstract = {<p>Plants produced natural generating products play a significant role in drug discovery of new bioactive compounds and these are used for advancement of innovative curative drugs for specific target health diseases. In this study Docking and ADME/T virtual screening method are apply for in drug discovery and can be divided into ligand- and target structure-based. The aim of this study was to analyze theDecalepis hamiltoniiisolated compounds by using the evaluation of molecular docking and virtual screening of anticancer drugs. MOE docking ADME/Toxicity and virtual screening approaches. A docking energy -12.97 kcal/mol; -9.93- kcal/mol on cancer responsible protein was targeted. Further, the compounds were filtered through the rule of five, ADME/Toxicity risk and synthetic accessibility. The active compound were then docked to recognize the possible target binding pocket to obtain a set of a ligand poses and to prioritize the predicted active compounds. The scrutinize compounds, as well as their metabolites were evaluated for different pharmacokinetics parameter such as ADME/Toxicity. Therefore, the result shows that a large number of compounds were found to be ADME/toxicity positive to be a positive drug molecule against cancer, selected compounds under study satisfies parameters for ADME and Toxicity properties. The present study demonstrate to identifying the novel structures which are having similar structural feature with like activity with respect to the compounds 3,5-Dimethyl-1,3,4-Hexanetriol and Dodecanoic acid that are shown best binding energy with the receptors 4igk and 4b3z respectively. This study may provide significant clues for discovery novel drug inhibitors for cancer properties.</p>},
	keywords = {ADME, Decalepis hamiltonii, docking, drug discovery, Toxicity, virtual screening},
	issn = {0739-1102},
	doi = {10.1080/07391102.2020.1778533},
	author = {Prakash, Palanisamy and Vijayasarathi, Durairaj and Selvam, Kuppusamy and Karthi, Sengodan and Manivasagaperumal, Rengarajan}
}
@article {47713,
	title = {On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities},
	journal = {Chemical Biology \& Drug Design},
	volume = {98},
	year = {2021},
	month = {AUG},
	pages = {258-269},
	type = {Article},
	abstract = {For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC(50) predictions can screen molecules that have desirable activity before assessment by docking studies.},
	keywords = {drug discovery, electron affinity, Electronegativity, molecular field descriptors, partial least squares, QSAR},
	issn = {1747-0277},
	doi = {10.1111/cbdd.13895},
	author = {Kunde, Pushkar D. and Ramkumar, Sudha and Kamble, Sanjay P. and RaviKumar, Ameeta and Kulkarni, Bhaskar D. and Kumar, V. Ravi}
}
