title: Three dimensional quantitative structure-toxicity relationship modeling and prediction of acute toxicity for organic contaminants to algae.
abstract: Although numerous chemicals have been identified to have significant toxicological effect on aquatic organisms, there is still lack of a reliable, high-throughput approach to evaluate, screen and monitor the presence of organic contaminants in aquatic system. In the current study, we proposed a synthetic pipeline to automatically model and predict the acute toxicity of chemicals to algae. In the procedure, a new alignment-free three dimensional (3D) structure characterization method was described and, with this method, several 3D-quantitative structure-toxicity relationship (3D-QSTR) models were developed, from which two were found to exhibit strong internal fitting ability and high external predictive power. The best model was established by Gaussian process (GP), which was further employed to perform extrapolation on a random compound library consisting of 1014 virtually generated substituted benzenes. It was found that (i) substitution number can only exert slight influence on chemical׳s toxicity, but low-substituted benzenes seem to have higher toxicity than those of high-substituted entities, and (ii) benzenes substituted by nitro group and halogens exhibit high acute toxicity as compared to other substituents such as methyl and carboxyl groups. Subsequently, several promising candidates suggested by computational prediction were assayed by using a standard algal growth inhibition test. Consequently, four substituted benzenes, namely 2,3-dinitrophenol, 2-chloro-4-nitroaniline, 1,2,3-trinitrobenzene and 3-bromophenol, were determined to have high acute toxicity to Scenedesmus obliquus, with their EC50 values of 2.5±0.8, 10.5±2.1, 1.4±0.2 and 42.7±5.4μmol/L, respectively.
authors: State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, PR China.
authors: Xiangqin
authors: X
authors: Jin
authors: Department of Mathematics, Heilongjiang Institute of Technology, Harbin 150050, PR China.
authors: Minghao
authors: M
authors: Jin
authors: State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, PR China. Electronic address: shenglx@nenu.edu.cn.
authors: Lianxi
authors: L
authors: Sheng
mesh_terms: D002708:Chlorella / Q000378:metabolism*; D008954:Models, Biological; D008956:Models, Chemical; D011525:Prototheca / Q000378:metabolism*; D044444:Scenedesmus / Q000378:metabolism*; D013329:Structure-Activity Relationship; D014874:Water Pollutants, Chemical / Q000737:chemistry*
chemical_list: D014874:Water Pollutants, Chemical
keywords: Acute toxicity; Alga; Machine learning; Organic contaminant; Three dimensional quantitative structure–toxicity relationship