Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints.

BACKGROUND: Quantitative and qualitative structure­activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented. OBJECTIVES: The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs. METHODS: A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots. RESULTS: Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles. DISCUSSION: Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

Stability and conformation of polycopper-thiolate clusters studied by density functional approach.

Quantum chemical studies of biologically relevant copper thiolate clusters can afford unique information about energetic principles of their formation and structure, which is important for understanding the basic principles of their formation and functioning in biological systems. In the current study, we used quantum chemical methods for the investigation of the structure and stability of Cu(x)S(y)-type clusters that serve as models for different copper thiolate clusters or for their intermediates in a variety of copper proteins. Density functional theory based modeling was performed including solvent effects for water and protein-like environments. Thermodynamic parameters (DeltaH, DeltaS, DeltaG) were calculated in order to assess the effect of thermal contributions to the formation energies of various copper thiolate clusters. The all-tricoordinated polycopper thiolate cluster [Cu4(SMe)6]2- turned out to be the most stable structure among the calculated ones. This result is in agreement with the prevalence of this type of clusters in various copper proteins with no sequence homology that contain six cysteine residues. The cooperativity of formation of [Cu4(SMe)6]2- can be inferred from the significant energy differences between intermediary clusters. Among tetrathiolate structures, [Cu2(SMe)4]2- was the most stable one. This cluster is also found in many copper proteins. Influence of slight structural perturbations on the energetics of copper thiolate clusters is also analyzed and discussed.