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.
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.
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.
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 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).