A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Rui Camacho 1 , Max Pereira 1 , Vítor Santos Costa 2 , 3 , Nuno A. Fonseca 2 , Carlos Adriano 1 , Carlos J. V. Simões 4 , 5  and Rui M. M. Brito 4 , 5
  • 1 LIAAD-INESC Porto LA & DEI, FEUP, Universidade do Porto, http://www.liaad.up.pt/, Portugal
  • 2 CRACS-INESC Porto, http://cracs.fc.up.pt/, Portugal
  • 3 DCC/FCUP Universidade do Porto, http://www.dcc.fc.up.pt/, Portugal
  • 4 Center for Neuroscience and Cell Biology (CNC), University of Coimbra, http://www.cnbc.pt/, Portugal
  • 5 Chemistry Department, University of Coimbra, http://www.uc.pt/fctuc/dquimica/, Portugal


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.

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Journal + Issues

The Journal of Integrative Bioinformatics is an international journal dedicated to methods and tools of computer science and electronic infrastructure applied to biotechnology. The journal covers mainly but not exclusively data/method integration, modeling, simulation and visualization in combination with applications of theoretical/computational tools and any other approach supporting an integrative view of complex biological systems.