DOI: 10.5281/zenodo.3540701
title: Modelling of ready biodegradability based on combined public and industrial data sources

description: The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues. In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %). The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions. Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website. This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed. This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]
description: {"references": ["NITE, Data from: Biodegradation and bioconcentration data under CSCL\u00b8 National Institute of Technology and Evaluation, 2007; dataset available at: https://www.nite.go.jp/en/.", "US EPA, Estimation Programs Interface Suite\u2122 for Microsoft\u00ae Windows v 4.11, US Environmental Protection Agency, Washington DC, USA, 2012; software available at: https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface.", "K. Mansouri, C.M. Grulke, R.S. Judson and A.J. Williams, OPERA models for predicting physicochemical properties and environmental fate endpoints, J. Cheminform. 10 (2018), pp. 1\u201319.", "E. Benfenati, A. Manganaro, and G. Gini, VEGA-QSAR: AI inside a platform for predictive toxicology. Proceedings of the workshop 'Popularize Artificial Intelligence 2013', December 5th 2013, Turin, Italy. 2013, published on CEUR Workshop Proceedings Vol 1107.", "OECD, Data from: eChemPortal: Global portal to information on chemical substances, Organisation for Economic Co-operation Development; dataset available at: https://www.echemportal.org/echemportal/index.action.", "J. Chem. Inf. Model. 53 (2013), pp. 867\u2013878", "J. Chem. Inf. Model. 52 (2012), pp. 655\u2013669"]}