DEMETRA is an EU-funded project. This project aim has been to develop predictive models and software which give a quantitative prediction of the toxicity of a molecule, in particular molecules of pesticides, candidate pesticides, and their derivatives. The input is the chemical structure of the compound, and the software algorithms use "Quantitative Structure-Activity Relationships" (QSARs). The DEMETRA software tool can be used for toxicity prediction of molecules of pesticides and related compounds. The DEMETRA models are freely available. Five models have been developed to predict toxicity against trout, daphnia, quail (oral and dietary exposure) and bee. The software is based on the integration of the knowledge acquired in the DEMETRA EU project in a homogeneous manner using the best algorithms obtained as the basis for hybrid combinative models to be used for predictive purposes.

The ECB dedicates activities to QSAR research and dissemination. Information on models and validation procedures can be found.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

The QSAR and Modeling Society provides several resources about databases, software, supercomputing centers, web services.

The EDKB program has developed computer-based models to predict affinity for binding of compounds to the estrogen and androgen nuclear receptor proteins. The process to develop the models was based on a close linkage of the laboratory and the modeling that resulted in training sets appropriately designed to calibrate models

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

AMBIT, a software for chemoinformatic data management, is an outcome of the LRI project:
'Building blocks for a future (Q)SAR ((Quantitative) Structure Activity Relationship) decision support system. Since the software goal is to support decisions, including chemical grouping and QSAR applicability domain appraisal, the name AMBIT was chosen meaning 'an area in which something acts or operates or has power or control'. The AMBIT software is available online and as a stand-alone application for beta testing.