At our European eCheminfo 2005 InterAction Meeting (9-10 November, Basel, Switzerland), we will address latest developments in the applications of Machine Learning & Graph Mining in Drug Discovery, through bringing together top computer science and cheminformatics experts with life science and pharmaceutical industry managers:
eCheminfo InterAction Meeting Session, Basel, 10 November 2005
Applications of Machine Learning & Graph Mining in Drug Discovery
chaired by Stefan Kramer (Technische Universitaet Muenchen)
Presenters & Discussion Leaders:
Christoph Helma (University of Freiburg)
Peter Willett (University of Sheffield)
Joost N. Kok (Leiden University)
Gisbert Schneider (Johann Wolfgang Goethe-University)
Michael Berthold (University of Konstanz)
The goal of this group and meeting session is to bring together researchers from graph mining and computational chemistry/cheminformatics/drug design. Graph mining experts will report on the state of the art and the possibilities offered by new techniques developed recently in this rapidly evolving field. Experts from cheminformatics and related areas will give an account of the challenges and pitfalls for graph mining techniques applied to chemical data. The technical part will cover issues such as: vertex and edge labels other than nominal, aggregates, 2D vs. 3D, feature selection, search techniques and pruning, overfitting avoidance, chemical similarity, lazy and instance-based learning, combining structured coding approaches with 3D information, including descriptions of pharmacophores and bioisosters based on field-based approaches, combining different coding approaches of ligands and protein targets, like consensus scoring, ensemble learning and multi-objective learning for ADME/Tox prediction, high-throughput docking studies and protein shape. The application areas will cover drug design and pharmocology, predictive toxicology and toxicogenomics, etc. The overall outcome of the meeting should be a set of matches between techniques and potential applications: Which new techniques could be useful where? Which new graph mining techniques can be readily applied to solve problems in cheminformatics? Which issues should be addressed by graph mining research? What are the challenges for graph mining approaches to be useful in real-world applications in this area?
Further program and registration information can be found on the eCheminfo website.
eCheminfo Community of Practice Manager