Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

mibPOPdb: An online database for microbial biodegradation of persistent organic pollutants

Microbial biodegradation of persistent organic pollutants (POPs) is an attractive, ecofriendly, and cost-efficient clean-up technique for reclaiming POP-contaminated environments. In the last few decades, the number of publications documenting POP-degrading microbes, enzymes, and experimental data sets has continuously increased, necessitating the development of a dedicated web resource that catalogs consolidated information on POP-degrading microbes and tools to facilitate integrative analysis of POP degradation data sets. To address this knowledge gap, we developed the Microbial Biodegradation of Persistent Organic Pollutants Database (mibPOPdb) by accumulating microbial POP degradation information from the public domain and manually curating published scientific literature. Currently, in mibPOPdb, there are 9215 microbial strain entries, including 184 gene (sub)families, 100 enzymes, 48 biodegradation pathways, and 593 intermediate compounds identified in POP-biodegradation processes, and information on 32 toxic compounds listed under the Stockholm Convention environmental treaty. Besides the standard database functionalities, which include data searching, browsing, and retrieval of database entries, we provide a suite of bioinformatics services to facilitate comparative analysis of users' own data sets against mibPOPdb entries. Additionally, we built a Graph Neural Network-based prediction model for the biodegradability classification of chemicals. The predictive model exhibited a good biodegradability classification performance and high prediction accuracy. mibPOPdb is a free data-sharing platform designated to promote research in microbial-based biodegradation of POPs and fills a long-standing gap in environmental protection research. Database URL: http://mibpop.genome-mining.cn/.

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH₃ > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pK_a and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

An improved opposition-based crow search algorithm for biodegradable material classification

The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.

Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees

Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative structure-activity relationship (QSAR) models have proposed effective solutions to this problem. However, the molecular descriptor datasets usually suffer from the problems of unbalanced class distribution, which adversely affects the efficiency and generalization of the derived models. Accordingly, this study aims at validating the performances of balanced random trees (RTs) and boosted C5.0 decision trees (DTs) to construct QSAR models to classify the ready biodegradation of substances and their abilities to deal with unbalanced data. The balanced RTs model algorithm builds individual trees using balanced bootstrap samples, while the boosted C5.0 DT is modeled using cost-sensitive learning. We employed the two-dimensional molecular descriptor dataset, which is publicly available through the University of California, Irvine (UCI) machine learning repository. The molecular descriptors were ranked according to their contributions to the balanced RTs classification process. The performance of the proposed models was compared with previously reported results. Based on the statistical measures, the experimental results showed that the proposed models outperform the classification results of the support vector machine (SVM), K-nearest neighbors (KNN), and discrimination analysis (DA). Classification measures were analyzed in terms of accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, F1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUROC).

Modelling of ready biodegradability based on combined public and industrial data sources

The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in silico prediction as valid alternative to 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 (Balance Accuracy (BA) = 0.74-0.79) and data coverage (83-91%). The Generative Topographic Mapping approach identified several chemotypes and structural motifs unique to the industrial dataset, 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. This is the biggest dataset reported in the literature so far, covering some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has larger applicability domain than the existing ones.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

Classification of biodegradable materials using QSAR modelling with uncertainty estimation

The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model's predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.