enviPath

enviPath is both, a database and a prediction system, for the microbial biotransformation of organic environmental contaminants. The database provides the possibility to store and view experimentally observed biotransformation pathways, and supports annotating pathways with experimental and environmental conditions. The pathway prediction system provides different relative reasoning models to predict likely biotransformation pathways and products.

Database

The enviPath database stores reviewed pathways from the scientific literature and predicted or user-entered pathways. The default package currently consists mainly of the pathways from the former EAWAG-BBD system. You can browse using the top menu. The database is organized in packages. Each package has an owner who can grant reading or writing permissions. We list data only as reviewed if it is reviewed by one of the organisations or groups in the reviewer group.

Prediction

enviPath can be used to predict biotransformation pathways. You can do this by simply using the input field on the start page. Enter a compound in SMILES format, or draw it using the molecule editor (by clicking on the dropdown on the left), and click on “Go!”. If the pathway for this compound was predicted before and is found in the database, a list of corresponding pathways will be returned, otherwise, the system will predict the pathway. Note that for anonymous users there is a limit to computation time and size of the predicted pathways. The resulting pathway will be stored in the database for 30 days and will be accessible and changeable for everyone. If you want to store the pathway for longer, prevent others from changing or seeing your pathways, or use more resources in terms of computation time and size of pathways, create an account (using the login button above) and set appropriate permissions for your data packages (the default settings should be suitable for most users).

enviPath is available at https://envipath.org. The development of enviPath and commercial support is handled by our company https://envipath.com.

2021

Tam, Jason; Lorsbach, Tim; Schmidt, Sebastian; Wicker, Jörg

Holistic Evaluation of Biodegradation Pathway Prediction: Assessing Multi-Step Reactions and Intermediate Products Journal Article

In: Journal of Cheminformatics, 13 (1), pp. 63, 2021.

Abstract | Links | BibTeX | Altmetric

@article{tam2021holisticb,

title = {Holistic Evaluation of Biodegradation Pathway Prediction: Assessing Multi-Step Reactions and Intermediate Products},

author = {Jason Tam and Tim Lorsbach and Sebastian Schmidt and J\"{o}rg Wicker},

url = {https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00543-x

https://chemrxiv.org/articles/preprint/Holistic_Evaluation_of_Biodegradation_Pathway_Prediction_Assessing_Multi-Step_Reactions_and_Intermediate_Products/14315963

https://dx.doi.org/10.26434/chemrxiv.14315963},

doi = {10.1186/s13321-021-00543-x},

year  = {2021},

date = {2021-09-03},

urldate = {2021-09-03},

journal = {Journal of Cheminformatics},

volume = {13},

number = {1},

pages = {63},

abstract = {The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Stepišnik, Tomaž; Škrlj, Blaž; Wicker, Jörg; Kocev, Dragi

A comprehensive comparison of molecular feature representations for use in predictive modeling Journal Article

In: Computers in Biology and Medicine, 130 , pp. 104197, 2021, ISSN: 0010-4825.

Abstract | Links | BibTeX | Altmetric

@article{stepisnik2021comprehensive,

title = {A comprehensive comparison of molecular feature representations for use in predictive modeling},

author = {Toma\v{z} Stepi\v{s}nik and Bla\v{z} \v{S}krlj and J\"{o}rg Wicker and Dragi Kocev},

url = {http://www.sciencedirect.com/science/article/pii/S001048252030528X},

doi = {10.1016/j.compbiomed.2020.104197},

issn = {0010-4825},

year  = {2021},

date = {2021-03-01},

journal = {Computers in Biology and Medicine},

volume = {130},

pages = {104197},

abstract = {Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

2017

Latino, Diogo; Wicker, Jörg; Gütlein, Martin; Schmid, Emanuel; Kramer, Stefan; Fenner, Kathrin

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data Journal Article

In: Environmental Science: Process & Impact, 2017.

Abstract | Links | BibTeX | Altmetric

@article{latino2017eawag,

title = {Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data},

author = {Diogo Latino and J\"{o}rg Wicker and Martin G\"{u}tlein and Emanuel Schmid and Stefan Kramer and Kathrin Fenner},

doi = {10.1039/C6EM00697C},

year  = {2017},

date = {2017-01-01},

journal = {Environmental Science: Process \& Impact},

publisher = {The Royal Society of Chemistry},

abstract = {Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies 

for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies
for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.

2016

Wicker, Jörg; Fenner, Kathrin; Kramer, Stefan

A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction Incollection

In: Lässig, Jörg; Kersting, Kristian; Morik, Katharina (Ed.): Computational Sustainability, pp. 75-97, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-31858-5.

Abstract | Links | BibTeX | Altmetric

@incollection{wicker2016ahybrid,

title = {A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction},

author = {J\"{o}rg Wicker and Kathrin Fenner and Stefan Kramer},

editor = {J\"{o}rg L\"{a}ssig and Kristian Kersting and Katharina Morik},

url = {http://dx.doi.org/10.1007/978-3-319-31858-5_5},

doi = {10.1007/978-3-319-31858-5_5},

isbn = {978-3-319-31858-5},

year  = {2016},

date = {2016-04-21},

booktitle = {Computational Sustainability},

pages = {75-97},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {One of the main tasks in chemical industry regarding the sustainability of a product is the prediction of its environmental fate, i.e., its degradation products and pathways. Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this chapter, we propose a hybrid knowledge-based and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. Since the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. The set of used rules is further extended using multi-label classification, where dependencies among the transformation rules are exploited to improve the predictions. While the results regarding recall and precision vary, the area under the ROC curve can be improved using multi-label classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and re-implementation of UM-PPS.},

keywords = {},

pubstate = {published},

tppubtype = {incollection}

}

Wicker, Jörg; Lorsbach, Tim; Gütlein, Martin; Schmid, Emanuel; Latino, Diogo; Kramer, Stefan; Fenner, Kathrin

enviPath - The Environmental Contaminant Biotransformation Pathway Resource Journal Article

In: Nucleic Acid Research, 44 (D1), pp. D502-D508, 2016.

Abstract | Links | BibTeX | Altmetric

2013

Wicker, Jörg

Large Classifier Systems in Bio- and Cheminformatics PhD Thesis

Technische Universität München, 2013.

Abstract | Links | BibTeX

@phdthesis{wicker2013large,

title = {Large Classifier Systems in Bio- and Cheminformatics},

author = {J\"{o}rg Wicker},

url = {http://mediatum.ub.tum.de/node?id=1165858},

year  = {2013},

date = {2013-01-01},

school = {Technische Universit\"{a}t M\"{u}nchen},

abstract = {Large classifier systems are machine learning algorithms that use multiple 

classifiers to improve the prediction of target values in advanced 

classification tasks. Although learning problems in bio- and 

cheminformatics commonly provide data in schemes suitable for large 

classifier systems, they are rarely used in these domains. This thesis 

introduces two new classifiers incorporating systems of classifiers 

using Boolean matrix decomposition to handle data in a schema that 

often occurs in bio- and cheminformatics. 

 

The first approach, called MLC-BMaD (multi-label classification using 

Boolean matrix decomposition), uses Boolean matrix decomposition to 

decompose the labels in a multi-label classification task. The 

decomposed matrices are a compact representation of the information 

in the labels (first matrix) and the dependencies among the labels 

(second matrix). The first matrix is used in a further multi-label 

classification while the second matrix is used to generate the final 

matrix from the predicted values of the first matrix. 

MLC-BMaD was evaluated on six standard multi-label data sets, the 

experiments showed that MLC-BMaD can perform particularly well on data 

sets with a high number of labels and a small number of instances and 

can outperform standard multi-label algorithms. 

Subsequently, MLC-BMaD is extended to a special case of 

multi-relational learning, by considering the labels not as simple 

labels, but instances. The algorithm, called ClassFact 

(Classification factorization), uses both matrices in a multi-label 

classification. Each label represents a mapping between two 

instances. 

Experiments on three data sets from the domain of bioinformatics show 

that ClassFact can outperform the baseline method, which merges the 

relations into one, on hard classification tasks. 

 

Furthermore, large classifier systems are used on two cheminformatics 

data sets, the first one is used to predict the environmental fate of 

chemicals by predicting biodegradation pathways. The second is a data 

set from the domain of predictive toxicology. In biodegradation 

pathway prediction, I extend a knowledge-based system and incorporate 

a machine learning approach to predict a probability for 

biotransformation products based on the structure- and knowledge-based 

predictions of products, which are based on transformation rules. The 

use of multi-label classification improves the performance of the 

classifiers and extends the number of transformation rules that can be 

covered. 

For the prediction of toxic effects of chemicals, I applied large 

classifier systems to the ToxCasttexttrademark data set, which maps 

toxic effects to chemicals. As the given toxic effects are not easy to 

predict due to missing information and a skewed class 

distribution, I introduce a filtering step in the multi-label 

classification, which finds labels that are usable in multi-label 

prediction and does not take the others in the 

prediction into account. Experiments show 

that this approach can improve upon the baseline method using binary 

classification, as well as multi-label approaches using no filtering. 

 

The presented results show that large classifier systems can play a 

role in future research challenges, especially in bio- and 

cheminformatics, where data sets frequently consist of more complex 

structures and data can be rather small in terms of the number of 

instances compared to other domains.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Large classifier systems are machine learning algorithms that use multiple
classifiers to improve the prediction of target values in advanced
classification tasks. Although learning problems in bio- and
cheminformatics commonly provide data in schemes suitable for large
classifier systems, they are rarely used in these domains. This thesis
introduces two new classifiers incorporating systems of classifiers
using Boolean matrix decomposition to handle data in a schema that
often occurs in bio- and cheminformatics.

The first approach, called MLC-BMaD (multi-label classification using
Boolean matrix decomposition), uses Boolean matrix decomposition to
decompose the labels in a multi-label classification task. The
decomposed matrices are a compact representation of the information
in the labels (first matrix) and the dependencies among the labels
(second matrix). The first matrix is used in a further multi-label
classification while the second matrix is used to generate the final
matrix from the predicted values of the first matrix.
MLC-BMaD was evaluated on six standard multi-label data sets, the
experiments showed that MLC-BMaD can perform particularly well on data
sets with a high number of labels and a small number of instances and
can outperform standard multi-label algorithms.
Subsequently, MLC-BMaD is extended to a special case of
multi-relational learning, by considering the labels not as simple
labels, but instances. The algorithm, called ClassFact
(Classification factorization), uses both matrices in a multi-label
classification. Each label represents a mapping between two
instances.
Experiments on three data sets from the domain of bioinformatics show
that ClassFact can outperform the baseline method, which merges the
relations into one, on hard classification tasks.

Furthermore, large classifier systems are used on two cheminformatics
data sets, the first one is used to predict the environmental fate of
chemicals by predicting biodegradation pathways. The second is a data
set from the domain of predictive toxicology. In biodegradation
pathway prediction, I extend a knowledge-based system and incorporate
a machine learning approach to predict a probability for
biotransformation products based on the structure- and knowledge-based
predictions of products, which are based on transformation rules. The
use of multi-label classification improves the performance of the
classifiers and extends the number of transformation rules that can be
covered.
For the prediction of toxic effects of chemicals, I applied large
classifier systems to the ToxCasttexttrademark data set, which maps
toxic effects to chemicals. As the given toxic effects are not easy to
predict due to missing information and a skewed class
distribution, I introduce a filtering step in the multi-label
classification, which finds labels that are usable in multi-label
prediction and does not take the others in the
prediction into account. Experiments show
that this approach can improve upon the baseline method using binary
classification, as well as multi-label approaches using no filtering.

The presented results show that large classifier systems can play a
role in future research challenges, especially in bio- and
cheminformatics, where data sets frequently consist of more complex
structures and data can be rather small in terms of the number of
instances compared to other domains.

2010

Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach Journal Article

In: Bioinformatics, 26 (6), pp. 814-821, 2010.

Abstract | Links | BibTeX | Altmetric

2008

Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Inproceedings

In: Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008.

Links | BibTeX