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With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. In this study a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into three activity groups, with respect to their RBA value, was carried out in a cascade of recursive partitioning trees, from descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and indices relying on charge distribution (max. partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appeared to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree.

Numerous chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect development and/or reproductive function in wildlife and humans. Due to the ability of these chemicals to interfere with the endocrine systems, they have been labeled as endocrine disruptors (EDs). SARs and QSARs are powerful screening tools to detect potential EDs and to prioritize them for more intensive and costly evaluations based on in vitro and in vivo assays. In this context, androgen-receptor binding data (active/inactive) for a large set of about 200 structurally diverse chemicals, described by CODESSA descriptors encoding topological and physicochemical properties, were used for deriving structure-activity models. Different types of artificial neural networks and support vector machines with different kernel functions were tested as statistical tools. The performance of a classical discriminant analysis was also estimated. The comparison exercise was performed on the basis of the same learning and testing sets as well as from the same set of selected descriptors. The modeling performances as well as the technical advantages and limitations of each statistical method have been critically analyzed.

There is increasing evidence that numerous chemicals released into the environment by human activities have the potential to alter the normal functions of the endocrine system in wildlife. These xenobiotics, mimicking natural hormones, are called endocrine disrupting chemicals (EDCs). The aim of this study was to catalogue the different EDC database (biological data, chemical descriptor data, etc.) resources available on the Internet for deriving structure-activity models. In vitro and in vivo experimental data on nuclear receptor binding assays, in vitro test methods reliability and variability assessment for detecting EDCs and comparison between biological assays will be presented and critically analysed.

Due to the abundance of data related to endocrine disruptors present in the environment, the free database "e-experimental endocrine disruptor binding assays" (e(3) dba) was designed to improve and to centralize the flow of information on experimental endocrine disruptor in vitro binding assays on nuclear receptors. It was designed with the aim to evaluate the validity of the existing QSARs and to establish new models. The e(3) dba database offers numerous interesting functionalities which are presented through selected graphical displays.

Nowadays the in silico scenario for drug design is totally independent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web.

a model of rainbow trout was built by homology with the human estrogen receptor. a high level of seqence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively.

The key to applying machine inference to scientific problems is to have an appropriate representation of the problem. The most successful applications of inductive logic programming (ILP) have been in the field of learning molecular quantitative structure activity relationships (QSARs). The basic representation used in these applications has been based on atoms linked together by bonds. This representation, although successful and intuitive, ignores the most basic discovery of 20th century chemistry, that molecules are quantum mechanical objects. Here we present a new representation using quantum topology (StruQT) for machine inference in chemistry which is firmly based on quantum mechanics. The new representation is based on Richard F.W. Bader's quantum topological atoms in molecules (AIM) theory. Central to this representation is the use of critical points in the electron density distribution of the molecule. Other gradient fields such as the Laplacian of the electron density can also be used. We have demonstrated the utility of the use of AIM theory (in a propositional form) in the regression problem of predicting the lowest UV transition for a system of 18 anthocyanidans. The critical point representation of fields can be easily mapped to a logic programming representation, and is therefore well suited for ILP. We have developed an ILP-StruQT method and are currently evaluating it for QSAR problems.