Compound toxicity detection software tools | Drug discovery data analysis

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.

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.