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Molecular Informatics|July 29, 2016
Back to the Roots: Prediction of Biologically Active Natural Products from Ayurveda Traditional MedicineHoney Polur, Tejal Joshi, Christopher T Workman, et al.Molecular Informatics|July 29, 2016
Using Local Models to Improve (Q)SAR PredictivityFabian Buchwald, Tobias Girschick, Madeleine Seeland, et al.Molecular Informatics|July 29, 2016
Structural Bioinformatics Approach of Cyclin-Dependent Kinases 1 and 3 Complexed with InhibitorsLucas A Saraiva, Marcia P Veloso, I Camps, et al.Molecular Informatics|July 29, 2016
On the Use of Local and Global QSPRs for the Prediction of Physico-chemical Properties of Polybrominated Diphenyl EthersEster Papa, Simona Kovarich, Paola GramaticaMolecular Informatics|July 29, 2016
Compound Design by Fragment-LinkingOsamu Ichihara, John Barker, Richard J Law, et al.Molecular Informatics|July 29, 2016
A Combination of Computational and Experimental Approaches to Investigate the Binding Behavior of B.sub Lipase A Mutants with Substrate pNPPZhong Ni, Xin Jin, Peng Zhou, et al.Molecular Informatics|July 29, 2016
In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR ApproachHai Pham The, Isabel González-Álvarez, Marival Bermejo, et al.Molecular Informatics|July 29, 2016
A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular StructureHuong Le-Thi-Thu, Yovani Marrero-Ponce, Gerardo M Casañola-Martin, et al.Molecular Informatics|July 29, 2016
A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression PredictionsUllrika Sahlin, Monika Filipsson, Tomas ÖbergMolecular Informatics|July 29, 2016
Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain InhibitorsH J Wiggers, J R Rocha, J Cheleski, et al.Pageof 101