Allows users to detect small molecules. FAF-Drugs is a web application which allows users to filter large compound libraries or determine some ADME-Tox properties (Adsorption, Distribution, Metabolism, Excretion and Toxicity). The software first standardizes the molecular structures before computing the different parameters. It can assist in hit selection before chemical synthesis or ordering as well as in silico screening experiments.
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Allows users to construct quantitative structure property/activity relationships (QSPR/QSAR). CORAL is a standalone software for building models from SMILES files according to the Monte Carlo method. It also includes some databases providing physicochemical parameters and biological activity which can be utilized to perform analysis or to create new models. CORAL can also analyze QSAR analysis, especially for detecting carcinogenic endpoint. The program is a part of the CHEMPREDICT project.
An integrated web-based platform for molecular descriptor and fingerprint computation. ChemDes provides more than 3,679 molecular descriptors that are divided into 61 logical blocks. In addition, it provides 59 types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints.
A web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions.
Allows to rationalize a prediction at the molecular level by analyzing the binding mode of the tested compound towards all 16 target proteins in real-time 3D/4D. The VirtualToxLab is an in silico tool for predicting the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. It simulates and quantifies their interactions towards a series of proteins known to trigger adverse effects using automated, flexible docking combined with multi-dimensional QSAR (mQSAR). Currently, the VirtualToxLab comprises 16 models of proteins known or suspected to trigger adverse effects: the androgen, aryl hydrocarbon, estrogen α, estrogen β, glucocorticoid, hERG, liver X, mineralocorticoid, progesterone, thyroid α, thyroid β and peroxisome proliferator-activated receptor γ as well as the enzymes CYP450 1A2, 2C9, 2D6 and 3A4.
Predicts many kinds of biological activity for compounds from different chemical series based on their 2D structural formulas. PASS finds new targets mechanisms for some ligands. It can reveal new ligands for some biological targets. The tool can be used to analyze the occurrence, in a database, of compounds predicted to be active for a well-defined set of PASS activities.
Gives accurate toxicity predictions quickly. Derek Nexus is a knowledge-based expert systems that predicts the toxicity and metabolism of a chemical, respectively. It offers an effective mechanism for the sharing of data and knowledge on chemical toxicity and metabolism. It also provides a more direct assessment of predictive performance, avoiding the inherent difficulties of reference to published studies, by allowing the user to access information directly on predictive performance for an alert within the version of the software.
A computational method to determine the vital elements of target RNA structure from mutagenesis and available high-resolution data.
Gives accurate mutagenicity predictions quickly. Sarah Nexus is a statistical-based system that uses a unique machine-learning methodology to build a statistical model for Ames mutagenicity. This novel system for the fragmentation of query structures enables greater transparency and aids interpretability of predictions in order to assist the user.
Allows pharmacophore identification. GUSAR is based on quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression (SCR) algorithm. It offers idea about the contribution of individual atom in deciding biological activity. The tool permits to point out favorable and unfavorable atoms by suitably coloring the atoms.
An open-source python package for calculating the commonly used structural and physicochemical features. ChemoPy computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently.
Allows prediction of general rodent carcinogenicity. ROSC-Pred is based on the PASS (Prediction of Activity Spectra for Substances) algorithm. It employs structural formula of organic compounds to proceed. PASS can be used to determine the biological activity spectra for drug-like organic molecules on the basis of their structural formulae. This tool employs the Structure-Activity Relationships database such as set of bases.
Predicts different kinds of toxic endpoints from toxicology related in vitro/in vivo experimental data and analyses toxic substructure. eMolTox is a web server that permits users to obtain information about potential molecular toxicities for safety analysis in drug development. The software offers several ways to submit query molecules, namely SMILES strings, IUPAC name, commercial name, CAS ID, InChI key or drawing a molecule.
Provides data from a variety of sources. Vitic Nexus is a chemical database and information management system. It offers researchers and scientists rapid access to searchable toxicological information. It evaluates the potential toxicity of existing or prospective chemicals through exploitation of large chemical libraries. It permits to improve the properties of a chemical in the R&D pipeline by slightly redesigning its molecular structure.
Helps for the prediction of complex toxicological endpoints, like carcinogenicity, long-term, and reproductive toxicity. lazar is a generic prediction algorithm for any biological endpoint with sufficient experimental data. It uses data mining algorithms to derive predictions for untested compounds from experimental training data. It also utilizes open source chemoinformatics libraries to calculate a range of physico-chemical descriptors.
Predicts and reports on toxicities for endpoints for an input chemical structure.
Builds and validates a predictive toxicity model based on an input toxicology dataset. ToxCreate is an OpenTox Framework application. It is aimed at researchers working in the life sciences and toxicology, QSAR experts, and industry and government groups supporting risk assessment, who are interested in building predictive toxicology models. It allows the creation of a number of models using one or more algorithms.
Classifies compounds as Carcinogens and Non-Carcinogens using only their two-dimensional structures. CarcinoPred-EL is a free carcinogenicity prediction online server. This web server has integrated three novel ensemble learning models, namely Ensemble XGBoost, Ensemble support vector machine (SVM) and Ensemble random forest (RF), to predict the carcinogenicity of chemicals. This method can be conveniently applied to the prediction of other toxicity endpoints or other pathological drug properties of chemicals.
Aims to foresee risk of drug-induced liver injury (DILI). DILI prediction models are based on a pattern recognition algorithm Decision Forest (DF) that uses a large set of drugs named DILIrank. It can be useful during preclinical development and for reducing hepatotoxicity related attrition. This tool uses a consensus modeling of multiple decision tree models. It was tested on 1 000 iterations of cross-validations and 1 000 bootstrapping.
Constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness.
An integrated framework for creating multiple regression models. RRegrs offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include multiple linear regression, generalized linear model with stepwise feature selection, partial least squares regression, Lasso regression, and support vector machines recursive feature elimination. The framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results.
Serves to assess chemically-induced skin sensitization. Pred-Skin is based on statistically significant and externally predictive quantitative structure-activity relationship (QSAR) models of skin sensitization. Its models use database which contains human and local lymph node assay (LLNA) data. The visualization of predicted fragment contribution is permitted thanks to a probability maps.
Implements algorithms for interpreting predictions made using the randomForest package. rfFC is an R package that calculates the feature contributions of an user dataset and an existing Random Forest model.
Serves as a Chembench module for quantitative structure-activity relationship QSAR toxicity predictions. MuDRA is able to develop accurate and interpretable models and its approach is related to the k-nearest neighbor (kNN) approach. Six different endpoints are concerned with this tool: Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization and endocrine disruption. It facilitates predictions of single compounds, batches of multiple compounds and virtual chemical libraries.
Allows to predict xenobiotic’s metabolism and calculates toxicity of metabolites based on the structural formula of chemicals products. The Metatox method uses dictionaries of biotransformation and is based on preliminary prediction of possible classes of biotransformation reactions. With this tool, nine reaction classes are catalyzed by five human cytochromes P450s and by human UDP-glucuronosyltransferase (UGT) without differentiation into isoforms.
Allows quantitative toxicity investigation and determination of small molecules. TopTox is based on the element specific topological descriptor (ESTD) method. It integrates physical descriptors with a variety of advanced machine learning algorithms, including two deep neural networks (DNNs) and two ensemble methods: the random forest (RF) method and the gradient boosting decision tree (GBDT). This tool builds topological learning strategies to proceed to its analyses.
Contains functionalities that support and evaluate experimental work and collaboration among modeling and computational research groups. JQ serves for modelling and analysis of engineered nanomaterials (ENMs). This tool allows user to store, share, and search data.
Provides a predictive model based on cytotoxicity effects to NIH/3T3 cells for multiple compounds. MouseTox is a web application that is useful for determining compounds’ risk assessment and for improving their virtual screening. It allows users to draw and submit a new structure for receiving the predicted cytotoxicty profile of each compound.
An in silico method, which is developed to predict and design toxic/non-toxic peptides.
Serves as a primary screening tool for dermatology and cosmetic research. SkinSense enables prediction of skin sensitization risk of molecules of interest. It provides mechanistic details such as skin protein reaction mechanisms and highlights reactive groups of molecules. This tool allows users to import test molecules, and classifies them into sensitizers and non-sensitizers.
Provides an easy-to-use graphical interface for quantitative structure-property relationship (QSPR) modelling. The BioPPSy program has 2 main functionalities, (i) the creation of a QSPR/QSAR model and (ii) the prediction of properties using this model. The reliability of QSPR and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. BioPPSy aims to fill the gap by providing an easy-to-use open-source software platform.
Identifies the types of conotoxins in targeting ion channels. iCTX-Type can become a useful high throughput tool for both basic research and drug development, particularly for in-depth investigation into the mechanisms of ion-channels and developing new drugs to treat chronic pain, epilepsy, spasticity, and cardiovascular diseases, among others. The overall accuracy of 91.07 per cent was achieved by using jack-knife cross-validation.
A full-featured and flexible user-friendly open source application, which is able to estimate toxic hazard by applying a decision tree approach.
A powerful and user-friendly software tool designed for the ranking of chemicals according to their environmental and toxicological concern based on the most recent ranking theories.
Allows users to profile and prioritize chemicals that integrates data from diverse sources. ToxPi consists of an algorithm that aims to investigate the effects of missing data and recommend solutions. This method was tested using simulated data motivated by high-throughput screening (HTS) data generated on chemicals in the substance priority list (SPL).
Predicts the conotoxin superfamily from the amino acid sequence. PredCSF is based on assembling different kinds of sequential features. It uses maximal overlap discrete wavelet transform (MODWT) and random forest approach to rank the physicochemical properties by importance. The tool is able to predict result with higher than 90% of success expectancy by testing it with the jack-knife test.
Evaluates the potential carcinogenicity of chemicals. OncoLogic is a structure–activity relationship (SAR) analysis programs which analyzes the chemical structure of a compound of unknown toxicity and predicts its likelihood to be a carcinogen based on comparisons to the structures of compounds with known toxicity, programmed chemical and toxicological expertise. The software assigns a baseline concern level ranging from low to high.
Predicts bacterial toxins and its function from primary amino acid sequence using SVM, HMM and PSI-Blast. Bacterial toxins play a vital role to cause disease and are responsible for majority of symptoms and lesions during infection.
A method for predicting neurotoxins and classifying them based on their function and origin.
Facilitates chemical similarity calculations. Toxmatch is an open-source software application that encodes several chemical similarity indices to facilitate the grouping of chemicals into categories and read-across. The software allows to compare datasets based on various structural and descriptor-based similarity indices and to calculate pair wise similarity between compounds or aggregated similarity of a compound to a set.
Provides a convolutional neural networks (CNNs) for cytotoxicity datasets. Tox-RCNN is based on a deep-learning approach for in vitro cell-based toxicity assessment. This program returns object detections that are organized as “healthy”, “toxicity affected” of “background” in entire field images. Its classification provides sensitive screening readouts that can pinpoint pre-lethal toxicity and has been tested for a array of toxicity pathways and cell assays.
Predicts toxicity state of cells under different treatments from microscopy images of fluorescently labeled nuclei by using convolutional neural networks (CNNs). Tox-CNN is based on a deep-learning approach for in vitro cell-based toxicity assessment. This software returns object detections that are classified either as “healthy”, or “toxicity affected” built on the probabilistic scores obtained at the output for both classes.
Predicts toxicity of molecules. TOXIpred integrates JME molecular editor that allows users to draw molecules of their choice. It allows to predict pIGC50 of a small chemical against Tetrahymena pyriformis. It can aid in the area of drug designing. Users can submit chemical molecules in the commonly used format as MOL, SMILE and SDF.
Performs toxicity prediction based on Deep Learning. DeepTox consists of: (1) cleaning and quality control of the data containing the chemical description of the compounds, (2) creation of chemical descriptors as input features for the models, (3) model selection including feature selection if required by the model class, (4) evaluation of the quality of models to choose the best ones, and (5) combination of models and ensemble predictors. The pipeline was built under the “Tox21 Data Challenge”.
Assists scientists in the area of product safety, hazard and risk assessment and toxicology to interactively evaluate the chemical reactivity, persistence, biodegradation and fate of chemical compounds in the environment.
Estimates aquatic toxicity. ECOSAR is a computerized version of the ecotoxicity analysis procedures as practiced by the Office of Pollution Prevention and Toxics (OPPT) when data are lacking for risk assessment development. The software, using computerized Structure Activity Relationships (SARs), allows to predict both short-term and long -term toxicity to aquatic organism. ECOSAR is menu-driven and contains various help functions to assist the user.
Uses a subset of P2 Assessment Framework computer-based tools to help identify chemicals that potentially may persist, bioaccumulate, and be toxic to aquatic life, i.e., PBT chemicals.
Estimates chemical releases and dose rates to humans from these releases.
Calculates the compound performance in experimental assays and animal models. TOPKAT exploits the molecular structure to measure and approve assessments of the toxic and environmental effects of chemicals. This software utilizes cross-validated quantitative structure toxicity relationship (QSTR) models for evaluating various measures of toxicity and interprets results via a patented Optimal Predictive Space (OPS) validation method.
Removes pan assay interference compounds (PAINS) from a chemical database.
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