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SAR and QSAR in Environmental Research|October 5, 2017
A sparse QSRR model for predicting retention indices of essential oils based on robust screening approachA M Al-Fakih, Z Y Algamal, M H Lee, et al.SAR and QSAR in Environmental Research|October 13, 2017
External validation of acute-to-chronic models for estimation of reproductive toxicity to Daphnia magnaA Furuhama, T I Hayashi, H Yamamoto, et al.SAR and QSAR in Environmental Research|October 13, 2017
Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformisJ A Castillo-Garit, G M Casañola-Martin, S J Barigye, et al.SAR and QSAR in Environmental Research|July 22, 2017
CATALOGIC 301C model - validation and improvementN H Dimitrova, I A Dermen, N D Todorova, et al.SAR and QSAR in Environmental Research|June 30, 2016
Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forestsX-W Zhu, Y-J Xin, Q-H ChenSAR and QSAR in Environmental Research|August 10, 2017
A SAR-based mechanistic study on the combined toxicities of sulfonamides and quorum sensing inhibitors on Escherichia coliC Chen, D Wang, H Wang, et al.SAR and QSAR in Environmental Research|November 22, 2017
Grouping of CoMPARA data with respect to compounds from the carcinogenic potency databaseM Vračko, V DrganSAR and QSAR in Environmental Research|November 30, 2017
In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning methodJ H Lee, S Basith, M Cui, et al.SAR and QSAR in Environmental Research|December 2, 2017
The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screeningB Sepehri, R GhavamiSAR and QSAR in Environmental Research|September 13, 2017
Microbial growth yield estimates from thermodynamics and its importance for degradation of pesticides and formation of biogenic non-extractable residuesA L Brock, M Kästner, S TrappPageof 134