The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

Hydrazones are popular building blocks in the development of functional materials because of their simple structure, straightforward synthesis, hydrolytic stability and tunable properties. Although a significant body of experimental information has been accumulated on the hydrazone-based rotary switches, mechanistic studies are still scarce. The effect of different stator units (phenyl-, naphthyl- and quinolinyl) on the on/off state distribution is studied in details at various computational levels in order to select a reliable computational level for a further computational study of the switching mechanism and rotary action of these systems. GRAPHICAL ABSTRACT

Тhe adequacy of chemical property predictions strongly depends on the structure representation, including the proper treatment of the tautomeric and isomeric forms. A combination of an in-house developed open-source tool for automatic generation of tautomers, Ambit-Tautomer, based on H-atom shift rules and standard quantum chemical (DFT) calculations is used for a detailed investigation of the possible geometric isomers, conformers and tautomers of unsubstituted and para-substituted phenylhydrazones, systems with experimentally observed unusual para-substituent effects on the intramolecular hydrogen bond (IMHB) for E-isomers of the compounds. The computational results show that the energetically preferred E-isomers are characterized by stronger IMHBs than the corresponding Z-isomers. The HN–N=C–C=N molecular fragment in the E-configurations is less sensitive to the substitution effect than the HN–N=C–C=O fragment in the isomers with Z-configuration. A probable reason for this decreased sensitivity of E-isomers to phenyl ring substitution is the more efficient conjugation and charge distribution in the HN–N=C–C=N fragment.