Frequently Asked Questions

1. ProTox-II

1.1 ProTox-II

ProTox-II is a virtual toxicity lab enabled to academic and non-commercial users via a web server, for the prediction of multiple toxicological endpoints related with a chemical structure. ProTox-II contains computer-based models trained on real data ( in vitro or in vivo ) to predict the toxic potential of the existing and virtual compounds. The acute toxicity class as well as different endpoints are calculated for an input compound based on chemical similarities to toxic compounds and trained machine learning models. ProTox-II envisage itself as a freely-available complete computational platform for in silico toxicity prediction for toxicologist, regulatory agencies, computational chemist and medicinal chemist.

1.2 Purpose of ProTox-II

ProTox-II is a free web service and the user can create a toxicity prediction for an input compound within a few minutes. Computational toxicity predictions can help to reduce the amount of animal experiments and save animal lives. ProTox-II incorporates molecular similarity and machine-learning models for various toxicity endpoints. A novelty of the ProTox-II webserver is that the prediction scheme is classified into different levels of toxicity such as oral toxicity (acute rodent toxicity), organ toxicity (hepatotoxicity), toxicological endpoints (such as mutagenicity, carcinotoxicity, cytotoxicity and immunotoxicity (B cell growth inhibition)), toxicological pathways (AOPs) and toxicity targets (Novartis off-targets) thereby providing insights into the possible molecular mechanism behind such toxic response.

Predict compound toxicity


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2. ProTox-Server

2.1 System Requirements

We recommend a recent version of Mozilla Firefox or Google Chrome, though the site should also be usable with Microsoft Internet Explorer (10,Edge) or Apple Safari. JavaScript has to be enabled to use all the features of the site. Depending on your browser and security settings, certain features like the Radar Chart might request you to permit the use of local browser storage for session data.
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2.2 Server Implementation

ProTox-II data is stored in a relational MySQL database. To handle the chemical information within the database, the MyChem package is used. For most of its functions, MyChem relies on the Open Babel toolbox. The website back-end is built using PHP; web access is enabled via the Apache HTTP Server. As an agile key/value store, Redis, is employed for queueing and assessing API requests.
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2.3 Using the API

For advanced users, data can be queried using a simple POST interface with a suitable language of your choice. Below, a short introduction and sample code in Python (Version 2.7) is provided. Please note that for single queries, the script is slower than the website, as it is set to allow several users a chance to queue their requests. The more models you require, the longer the query intervals take due to computation time. A source IP is allowed a maximum of 250 API queries a day.
You can download this script to your local computer and use it, or write your own with the script as a reference : Sample API Script

To run the script, you would need to install python on your system, and invoke your command line (either via cmd on windows, or opening a terminal on linux or mac os). The interface allows you to query by name (fulfilled via PubChem search) or canonical SMILES string. As a minimum, you need only enter one or more such identifiers (separated by comma).
If you prefer no status outputs save errors, use the -q command line switch.

python aspirin,vorinostat
Simple example - Query default data (acute toxicity, toxicity targets) using default input type (pubchem name search), for the drugs Aspirin and Vorinostat

Additional data can be supplied using command line switches, from specifying the input type (if you want to input canonical SMILES), to selecting the models (you can see a full list of models either in the Toxicity Model Report Table, in the Shorthand column, or in the script itself, in the ALL_MODELS declaration)

python -t smiles -m acute_tox,tox_targets,ALL_MODELS -o out.csv "CCC(=C(C1=CC=CC=C1)C2=CC=C(C=C2)OCCN(C)C)C3=CC=CC=C3"
Customized example : query server for all model data, based on a smiles-string, and output to out.csv.
PLEASE NOTE : As seen below, add quotation marks if you include a SMILES string. Likewise,use quotes around the whole query if split drugnames (two words) occur.

The API by default returns data in the form of JSON strings. These objects can easily be unpacked in most languages, and allow for convenient transfer of nested arrays. The following hierarchical components are provided as keys in each response written to your outfile:
    id : The request id that was used to retrieve this dataset, marking each individual request
    name : if using name input type, the compound name requested, otherwise empty string
    smiles : if using canonical SMILE input type, the input smiles, otherwise empty string
    acute_tox : If selected, the acute toxicity prediction with LD50, toxicity class and prediction accuracy data
        ld50: predicted ld50 in mg/kg
        tox_class : predicted toxicity class (1-6)
        avg_similarity : average similarity in % (float 0-100)
        pred_accuracy : predicted accuracy in % (float 0-100)
    tox_targets : If selected,toxicity targets with similarity values
        abbreviation : Short-Form of toxicity target (e.g. ANDR, AOFA, CRFR1, ..., like on the website)
        tox_target : Full name of the toxicity target
        average_similarity_known_ligands : Similarity score in % (float 0-100)
        binding_probability_class: 0-3 (0=no binding, 3=probable binding), color-coded on website
        average_pharmacophore_fit: Fit score in % (float 0-100)
    tox_models : If selected, Data for all other computable models with name, prediction and prediction confidence
        [model name] : Shorthand name of the model
            Prediction : Boolean value if activity or inactivity is predicted (1=Active, 0=Inactive)
            Probability : Float value from 0 to 1 giving confidence of the above result

Please note the output is intended to be machine-readable. To inspect it manually, using a JSON-Viewer like JSON viewer or Code Beautify JSON Viewer is recommended.
However, otherwise, the website itself is more suitable to such visualization.
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3. Biological Background

3.1 Toxicity prediction

The investigation of the absorption, distribution, metabolism, excretion and toxicity, the so-called ADMET properties of a compound, is a crucial step in the drug development process. Before a drug candidate proceeds into clinical trials, its ADMET properties have to be determined. Usually, toxicities are investigated in animal experiments which are time-consuming and take animal lives. In silico toxicity predictions are a fast and inexpensive alternative to animal experiments. They rely on known toxicity data which is used to develop a model capable of predicting toxicities of new compounds. On the other hand, mechanism-based prediction and evaluation of chemical toxicity is still an evolving science, and such understanding is important for the development of drugs as well as regulatory decisions. A particular compound can be active for multiple toxicity endpoints. A chemical that interacts with a protein as an off-target, can also interact with multiple proteins with different affinities, consequently it can activate different signalling pathways or interact with multiple functional pathways. The signaling or functional pathways that are perturbed may have overlapping connectivity, resulting in synergistic or canceling system consequences. Similarly this can extend to across organ, tissues, cellular levels of connectivity, resulting in servere and strong toxic profiles (P.F. Bai et al. 2013).
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3.2 Toxicity classes

Based on the severity of their effect, compounds can be classified into different toxicity groups (classes). As explained here, our webserver uses the GHS toxicity classification whereby compounds are divided into 6 classes - 5 classes representing different grades of toxicities as well as the non-toxic class.
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3.3 Toxic fragments

Toxic fragments were generated using ROTBOND and RECAP method. There were about 37000 distinct fragments created from toxic and non-toxic compounds present in the training data set. Based on statistical analysis on the occurence of each fragment, fragments were further classified as specific fragments with respect to the toxicity classes. Additionally, these specific fragments are used to predict the toxic class for the input molecule. Some examples of molecules containing the specific toxic fragments are Perfluoroterephthalonitrile, Chloroflurazole, Benzimidazole, 4,5,7-trichloro-6-nitro-2-(trifluoromethyl) etc. Few examples of specific toxic fragments present in our training dataset are shown below:

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3.4 Acute Toxicity

Acute toxicity describes the adverse effects of a substance that result either from a single exposure or from multiple exposures in a short period of time (e.g. less than 24 hours). The acute oral toxicity prediction results are based on the analysis of 2D similarities and the recognition of toxic fragments in approximately 38, 000 unique compounds with known oral LD50 values measured in rodents.

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3.5 Organ Toxicity

Chemicals that can cause adverse effects or disease states manifested in specific organs of the body is defined as organ toxicity.
Hepatotoxicity refers to liver dysfunction or liver damage that is associated with an overload of drugs or xenobiotics. The liver cell injury can be due to a multitude of causes including drugs, toxins, herbal and dietary supplements, and other agents.
The organ toxicity prediction results are based on the trained machine learning model using Random Forest Classifier and discriminative features. Additionally, future considerations of additional organ based toxicities such as cardiotoxicity, neurotoxicity are planned.
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3.6 Toxicological Endpoints

ProTox-II currently includes methods for prediction of four toxicological endpoints such as cytotoxicity, mutagenicity, carcinogenicity and immunotoxicity.
Chemicals that change the genetic material, usually DNA of an organism are defined as mutagen and the adverse effects is called mutagenicity.
Chemicals that can cause cells to become cancerous by altering their genetic structure so that they multiply continuously and become malignant are called carcinogens and the adverse effects is called carcinogenicity.
Chemicals that alters the functioning of the immune system upon exposure are called immunotoxins and the adverse effect is called immunotoxicity. The current immunotoxicity model is based on B cell growth inhibition. Additonal model on T cell inhibition will be added soon.
All the models are based on machine learning methods and the results are predicted with a confidence score.
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3.7 Toxicological Pathways

Toxicology in the 21st Century (Tox21) is a federal collaboration among EPA, NIH, including National Center for Advancing Translational Sciences and the National Toxicology Program at the National Institute of Environmental Health Sciences, and the Food and Drug Administration.
According to the Tox21 Consortium, chemical compounds might have the potential to disrupt processes in the human body that may lead to negative health effects.
The researchers at Tox21 consortium have tested 10,000 environmental chemicals (called the Tox21 10K library) for their potential to disrupt biological pathways that may result in toxicity, this associated pathways are called adverse outcome pathways (toxicological pathways). The Tox21 data challenge which was hosted in the year 2014, consisted of 12 pathways based on cellular assays, under two types of pathways. The idea behind the approach is that a chemical compound when interacts with the receptors, enzymes etc (either activate/inhibit) can result in perturbation in the biological pathways and thereby disrupt the cellular process causing cell death.
The two pathways namely defined as
1)Nuclear Receptor Signalling Pathways (7 pathway assays)
2)Stress Response Pathways (5 pathway assays)
Important: Many compounds in Tox21 10K library have shown cytotoxicity in a lower concentration than the concentration needed to interact with a receptor. As mentioned in the paper (Judson et al. 2013), some of the compounds might kill the cells before having any action on the receptor. Since these information are not considered in the model training (due to data/information inavailability), the users are requested to keep this information in mind and output of these models should be considered with caution.
More information on Tox21 pathways can be found here.
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3.8 Toxicity targets

Compound toxicity can be caused by many different mechanisms. Adverse toxicological effects are often categorized as chemical-based, on-target, or off-target effects. Chemical-based toxicity is defined as toxicity that is related to the physicochemical characteristics and structure of a compound and its toxic effects on cellular organelles, membranes, and/or metabolic pathways. On-target refers to exaggerated and adverse pharmacological effects at the target of interest in the system (Rudmann et al. 2013). Off-target refers to adverse effects as a result of modulation of other targets; these may be related biologically or totally unrelated to the target of interest. It is imperative to use the toxicological and biological data on the target to form testable hypotheses related to whether a toxicity is chemical-based, on-target, or off-target. To understand the underlying mechanism, it is important to consider the macromolecular targets to which a compound binds. Some targets are important for the therapeutic effect of the drug compound. Other targets, so-called 'off-targets' or 'tox targets' are responsible for adverse drug reactions and toxicities associated with a drug compound.

Which toxicity targets are we considering?
Tox targets have been defined according to the Novartis in vitro safety panel of targets associated with adverse drug reactions (Lounkine et al. 2012). 73 toxicity diverse toxicity targets are considered, including transmembrane proteins as well as intracellular receptors. A list of all toxicity targets is available here.
Currently, only protein targets for which experimental structures of human protein-ligand complexes have been solved, are considered for prediction. These include:

- Adenosine A2A receptor (2 toxicophores),
- Adrenergic beta 2 receptor (7 toxicophores),
- Androgen receptor (46 toxicophores),
- Amine oxidase (3 toxicophores),
- Dopamine D3 receptor (1 toxicophore),
- Estrogen receptor 1 and 2 (195 and 32 toxicophores),
- Glucocorticoid receptor (7 toxicophores),
- Histamine H1 receptor (1 toxicophore),
- Nuclear receptor subfamily 1 group I member 2 (97 toxicophores),
- Opioid receptor kappa (4 toxicophores),
- Opioid receptor mu (1 toxicophore based on homology model),
- cAMP-specific 3',5'-cyclic phosphodiesterase 4D (152 toxicophores),
- Prostaglandin G/H synthase 1 (1 toxicophore),
- Progesterone receptor (9 toxicophores).

However, future considerations of additional targets as well as other types of pharmacophore models are planned.

How are toxicity targets predicted?
Tox targets are represented in form of pharmacophore models. For each toxicity target, a set of of pharmacophore models is generated and validated using a set of active compounds and property-matched decoys. Only pharmacophores receiving a good validation result are used further. The fit value of a pharmacophore to a compound is then used as an indicator of the strength of binding to a specific target.
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3.9 Performance analysis

The performance of a binary prediction method can be assessed retrospectively, using a validation set with known activities. For example, when considering the prediction whether a compound is in toxicity class 1 or is not in toxicity class 1, the following four values can be determined: the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). From these numbers, the following performance parameters can be calculated:

1) Sensitivity/ Hit rate/ Recall/ True positive rate: TP/all positives in validation set
2) Specificity/ True negative rate: TN/all negatives in validation set
3) Precision/ Positive predictive rate: TP/(TP+FP)
4) Balanced accuracy: (Sensitivity+specificity)/2
5) Kappa index measures the quality of the binary classification models
6) The area under the curve (AUC) of a receiver operating characteristic (ROC): curve plots the sensitivity versus 1-specificity at different threshold

7) Identification of frequent features in active and inactive compounds: To analyse the important and frequent features in active and inactive compounds. The percentage of occurrences of each feature from Morgan fingerprint (2,048 bits) in active and inactive compounds was calculated. The relative frequency of important features for a class (e.g., active) were calculated taking not only the feature position and occurrence within the active class into account but also the relative feature frequency of that feature in the inactive class and vice versa. The average relative frequency for each class were calculated, a feature was only considered important for a class, if it's presence in one class is higher than the average relative frequency of that class as well as lower than the average relative frequency of the other class. This work has been reported in our published work (Banerjee P and Preissner R (2018)).

8) Sampling method: A selective oversampling of minority class is introduced in the construction of the models. For each of the prediction end-points, the active (positive) and inactive (negative) data are fragmented using RECAP and ROTBONDS fragmentation methods. The propensity score (PS) for each of the uniquely occurring fragments in both the sets is computed. Only those molecules having the highest propensity scores for fragments conserved for the active class are randomly chosen to be duplicated and added to the original data set. (This in turn reduces the variance). Both steps are repeated until the minority class consists of as many samples as the majority class for all the models.

9) Fragment propensity based CLUSTER cross-validation : Often an entire molecule may not be responsible for the activity, but a local feature (such as a substructure or a fragment) in a molecule may be responsible for the desired response. Chemical fragments are local parts of chemical structures, representing molecular features useful in the modelling of biological or physiochemical properties of chemicals. Therefore, our models take into consideration of local similarity when compared to overall (complete) similarity between set of two active or two inactive compounds for sampling and cross-validation partition. Fragments propensities were mainly used to detect the meaningful features and to capture continuous relationships that exist between the fragments of the same class. They offer intuitive interpretation of the model performance, easy to generate and handle. The 10-fold cross-validation for all the models were performed using fragment- based similarity of compounds. The fragment propensities were calculated for both active and inactive class, as continuous real-valued numbers in the range between 0= low and 1= high. The compounds were thus group into 10 parts based on the fragment propensities. Thus, the group of compounds sharing the fragments propensities were distributed across the folds. The compounds with unassigned fragment propensity were then randomly assigned across the fold. The Compounds assignment to the different folds was done ensuring fragment similarity of compounds and similar ratio of actives to inactives in all the folds, including training and test set.

In our acute toxicity model, we are not only interested in the prediction of one toxicity class, but all of the six toxicity classes. Therefore, we have calculated the sensitivity, specificity and precision for all toxicity classes considered. The overall sensitivity, specificity and precision values have been calculated as averages weighted by the number of compounds in the validation set which belong to a specific class.

For other models, since some of the datasets were imbalanced (having minority and majority class), we have used balanced accuracy and AUC-ROC to access the models)
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4. Tutorial

This tutorial shows how to run a toxicity prediction and how to interpret the results. If you have any questions, which are not answered in the FAQs, please feel free to contact us!

4.1 How to run a toxicity prediction

To start a toxicity prediction, please go to Tox Prediction. Here, you can either draw your input compound, paste the content of a molfile in textform or search for a compound name online:

To draw a chemical structure, use the buttons in the second row (as shown above). You can change atom types by clicking on the arrow next to "C" or change bond types or draw ring structures. Please note that a carbon atom is already drawn (grey dot in the middle of the drawing area). To open a molfile, please click on the yellow folder button in the first row (as shown above). You can paste the contents of a molfile (text from) here. You can also search for a known compound online. To do that, click on the binocular in the first row of buttons (as shown above). You can search for a compound name in the PubChem database.
An example compound is already mentioned e.g. Etonogestrel. To use the example compound, simply type the name and click on name search Start Tox-Prediction. To clear the drawing area, press the button with the blue bottle in the first row of buttons.
Once you have drawn or inserted an input molecule, you can start the toxicity prediction by clicking on the Start Tox-Prediction button below the drawing area. Additionally, you can select the models of your choice or all the models mentioned for prediction. If you are only interested in acute toxicity and toxicity targets of your chemical compounds, the server by default calculates that.

Please note that the prediction of toxicities of multiple compounds can be time-consuming. An estimation of the calculation time is given at the top of the results page, but in case the user does not want to wait for the results, the results page can be bookmarked and the results accessed at a later time. The results (predicted LD50 values, predicted toxicity class, etc.) are given in a tabular format. Further details, including similar compounds with known toxic class and possible toxicity targets, Toxicity model report can be obtained for each compound by clicking on the 'plus button'.

A toxicity radar plot (example below) is provided to assess the comparison between the different toxicity models active compounds average probability from the training set to that of the input compound. The plot can be accessed using the 'Open Toxicity Radar Chart' link that will appear on top of the page once the computation is complete, which will open the chart in a new tab. The toxicity profile of the input compound is shown using blue lines/dots which represents the predicted probabilities of the input compound for respective ProTox-II models. The data displayed is orange dots/lines is the average probability of its active class, acquired by computing from the training set data for each model (see model info). For the example case Etonogestrel, the predicted probabilities for the models AR, AR-LBD, ER, ER-LBD is higher than the average probabilities for each of these models, representing a strong prediction confidence. Similarly this can be observed for the Immunotoxicity model. However, the prediction probability for the Hepatotoxicity model is lower than the average probability of the training set active compounds. This chart helps the user to get an understanding, how strong is the overall prediction of the input compound, considering its activity for multiple toxicity endpoints.

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4.2 Toxicity prediction results

The results of the toxicity prediction will open automatically after a few moments. The report for your input compound will look like this:

The structure of your input compound is shown in the box on the left whereas some properties of the input compound are displayed in the table on the right. The prediction results are shown in the middle of the page. A prediction for the median lethal dose (LD50) is given in mg/kg body weight at the top, followed by the toxicity class. 6 different toxicity classes are distinguished, as explained here, and each class is displayed in a different color (see box).
Furthermore, a prediction accuracy is calculated and displayed. The more saturated the prediction accuracy box color, the higher the accuracy (see box below). The prediction accuracy depends on the similarity of the input compound to compounds with known LD50 values as well as the hit rates obtained in a cross-validation study.

In addition to the prediction results, some information about the input compound is given. The diagram on the left indicates the molecular weight (MW) distribution of compounds in our dataset. The mean MW is indicated as red line whereas the MW of the input compound is indicated as black line. In the diagram on the right, the distribution of LD50 values of our dataset is shown. Again, the mean of our dataset is shown in red and the predicted median lethal dose of the input compound is shown in black.

Below the diagrams, 3 compounds which are the most similar compounds of our dataset to the input molecule are displayed. Their chemical structures as well as their properties are shown. Please note that the toxicity class is assigned based on 3 different schemes. First of all, if multiple LD50 values are available for one compound, the toxicity class can be assigned based on the minimum (min) or the average (avg) dose. Secondly, the toxicity class can be calculated based on the concentration, taking into account the molecular weight of the compound.

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4.3 Toxicity model report

The toxicity model report gives an overview of the predicted activity computed by the various machine learning models. After the calculations are completed for multiple models, the result looks like below :

Targets are sorted by the leftmost classification. A target predicted to be active with the input molecule will be emphasized by a bolded prediction tag. The probability on the left hand side gives a confidence estimate for the prediction. Data points with a confidence below 70% (0.7) are normally omitted, displaying a value of "Below Threshold". While the model is still computing, a value of "Calculating..." is shown here. The top of the page should give an estimated of when the models are finished, but at most, it should take around 2 minutes, less if part or all of the selected models already has been precomputed in the past. Not selected models are marked with "Not Calculated".

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4.4 Toxicity target indication

The results of the toxicity target alert will show up at the toxicity prediction report page and will look similar to the image below:

The first table gives an overview over all investigated toxicity targets. The target name abbreviations are given in the first row and they contain hyperlinks to further information about the protein targets. The colors indicate how probable binding to the toxicity target is: black indicates no binding, whereas yellow, orange and red indicate possible binding. The more intense the color, the more probable the binding is.
If toxicity targets are found for an input ligand, a second table provides the details of the targets found. The target name as well as its average pharmacophore fit and average similarity to known ligands of this target (based on Tanimoto similarity) are given. The average pharmacophore fit indicates how well compounds similar to the input compound can fit the protein-ligand-based pharmacophores developed for every target. The average similarity, on the other hand, indicates the similarity of the input compounds to molecules which have been shown to bind at this particular target.
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