Towards FAIR nanosafety data.

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database.

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.

Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling.

Ambit-GCM is a new software tool for group contribution modelling (GCM), developed as a part of the chemoinformatics platform AMBIT. It is an open-source tool distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-GCM provides an environment for creating models of molecular properties using additive schemes of zero, first or second orders. Ambit-GCM supports a set of local atomic attributes used for dynamic configuration of desired atom descriptions, which are applied to define fragments of different sizes. All defined groups are exhaustively generated for each molecule from a training set of compounds and combined to form the basic set of GCM fragments. Additionally, Ambit-GCM users can define correction factors via custom SMARTS notations or add externally calculated molecular descriptors. A molecular property model is obtained as a sum over all found groups by multiplying each group or correction factor frequency to its corresponding contribution. Multiple linear regression analysis (MLRA) is used for group contributions calculation. Ambit-GCM performs full statistical characterization of the obtained MLRA models via various validation techniques: external tests validation, cross validation, y-scrambling, etc. The software can be optionally used only for molecule fragmentation combined with an external statistical modelling package for further processing. Ambit-GCM example usage and test cases are given.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

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.

Ambit-Tautomer: An Open Source Tool for Tautomer Generation.

We present a new open source tool for automatic generation of all tautomeric forms of a given organic compound. Ambit-Tautomer is a part of the open source software package Ambit2. It implements three tautomer generation algorithms: combinatorial method, improved combinatorial method and incremental depth-first search algorithm. All algorithms utilize a set of fully customizable rules for tautomeric transformations. The predefined knowledge base covers 1-3, 1-5 and 1-7 proton tautomeric shifts. Some typical supported tautomerism rules are keto-enol, imin-amin, nitroso-oxime, azo-hydrazone, thioketo-thioenol, thionitroso-thiooxime, amidine-imidine, diazoamino-diazoamino, thioamide-iminothiol and nitrosamine-diazohydroxide. Ambit-Tautomer uses a simple energy based system for tautomer ranking implemented by a set of empirically derived rules. A fine-grained output control is achieved by a set of post-generation filters. We performed an exhaustive comparison of the Ambit-Tautomer Incremental algorithm against several other software packages which offer tautomer generation: ChemAxon Marvin, Molecular Networks MN.TAUTOMER, ACDLabs, CACTVS and the CDK implementation of the algorithm, based on the mobile H atoms listed in the InChI. According to the presented test results, Ambit-Tautomer's performance is either comparable to or better than the competing algorithms. Ambit-Tautomer module is available for download as a Java library, a command line application, a demo web page or OpenTox API compatible Web service.