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SAR and QSAR in Environmental Research|October 26, 2020
Computational screening of natural and natural-like compounds to identify novel ligands for sigma-2 receptorM A Alamri, O Afzal, M A AlamriSAR and QSAR in Environmental Research|February 6, 1999
Structure-toxicity relationships for aminoalkanols: a comparison with alkanols and alkanaminesG D Sinks, T A Carver, T W SchultzSAR and QSAR in Environmental Research|August 29, 2019
Could deep learning in neural networks improve the QSAR models?G Gini, F Zanoli, A Gamba, et al.SAR and QSAR in Environmental Research|February 1, 2021
SAR and QSAR research on tyrosinase inhibitors using machine learning methodsY Wu, D Huo, G Chen, et al.SAR and QSAR in Environmental Research|January 19, 2021
Endocrine disruption: the noise in available data adversely impacts the models' performanceF Lunghini, G Marcou, P Azam, et al.SAR and QSAR in Environmental Research|January 18, 2021
Computational identification of chemical compounds with potential anti-Chagas activity using a classification treeJ A Castillo-Garit, S J Barigye, H Pham-The, et al.SAR and QSAR in Environmental Research|October 1, 2019
Toxicity profiling and prioritization of plant-derived antimalarial agentsJ Devillers, H DevillersSAR and QSAR in Environmental Research|October 22, 2016
Acute to chronic estimation of Daphnia magna toxicity within the QSAAR frameworkA Furuhama, T I Hayashi, N TatarazakoSAR and QSAR in Environmental Research|February 20, 2019
Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo methodManisha, S Chauhan, P Kumar, et al.SAR and QSAR in Environmental Research|February 23, 2019
Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSARP Kumar, A Kumar, J SindhuPageof 134