Abstract
One of the most important advantages of logic programming systems is that they allow the transparent exploitation of parallelism. The different forms of parallelism available and the complex nature of logic programming applications present interesting problems to both the users and the developers of these systems. Graphical visualisation tools can give a particularly important contribution, as they are easier to understand than text based tools, and allow both for a general overview of an execution and for focusing on its important details. Towards these goals, we propose VisAll, anew tool to visualise the parallel execution of logic programs. VisAll benefits from a modular design centered in a graph that represents a parallel execution. A main graphical shell commands the different modules and presents VisAll as an unified system. Several input components, or translators, support the well-known VisAndor and VACE trace formats, plus a new format designed for independent and-parallel plus or-parallel execution in the SEA. Several output components, or visualisers, allow for different visualisations of the same execution.

Abstract
It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal--iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.

Abstract
Structural activity prediction is one of the most important tasks in chemoinformatics. The goal is to predict a property of interest given structural data on a set of small compounds or drugs. Ideally, systems that address this task should not just be accurate, but they should also be able to identify an interpretable discriminative structure which describes the most discriminant structural elements with respect to some target. The application of ILP in an interactive software for discriminative mining of chemical fragments is presented in this paper. In particular, it is described the coupling of an ILP system with a molecular visualisation software that allows a chemist to graphically control the search for interesting patterns in chemical fragments. Furthermore, we show how structural information, such as rings, functional groups such as carboxyls, amines, methyls, and esters, are integrated and exploited in the search.

Abstract
This chapter introduces inductive logic programming (ILP) from the perspective of search algorithms in computer science. It first briefly considers the version spaces approach to induction, and then focuses on inductive logic programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of inductive logic programming will help the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the State-of-the-Art in the domain. © 2008, IGI Global.

Abstract
"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k?-?medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
The rational development of new drugs is a complex and expensive process. A myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognised as the major hurdle behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship studies, using relational Machine Learning algorithms, proved already to be very useful in the complex process of rational drug design. However, a typical problem with those studies concerns the use of available repositories of previously studied molecules. It is quite often the case that those repositories are highly biased since they contain lots of molecules that are similar to each other. This results from the common practice where an expert chemist starts off with a lead molecule, presumed to have some potential, and then introduces small modifications to produce a set of similar molecules. Thus, the resulting sets have a kind of similarity bias. In this paper we assess the advantages of filtering out similar molecules in order to improve the application of relational learners in Structure-Activity Relationship (SAR) problems to predict toxicity. Furthermore, we also assess the advantage of using a relational learner to construct comprehensible models that may be quite valuable to bring insights into the workings of toxicity.