This Week’s Discoveries | 14 February 2017
- 14 February 2017
- Oort Building
Niels Bohrweg 2
2333 CA Leiden
- De Sitterzaal
Killing one bird with two stones
Laura Heitman (LACDR) is an associate professor of Molecular Pharmacology at the Division of Medicinal Chemistry (LACDR). Her research interests are mainly focused on understanding and improving drug-target interactions, and more specifically, target binding kinetics and allosteric modulation of G protein-coupled receptors. Today, she will talk about her recent publication in Nature, entitled: “Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists”, and its importance for drug discovery.
Understanding Complexity in Multicriteria Analysis: Fundamental Limits and Efficient Algorithms
Michael Emmerich (LIACS) is associate professor at the Leiden Institute of Advanced Computer Science (LIACS), where he is leading the Multicriteria Optimization and Decision Analysis research group. He is also the LIACS coordinator of the European Research Project on Information Systems (ERCIS), and the Leiden Data Driven Drug Discovery Network (D4N) and involved in the Leiden Complex Network Network (LCN2).
Multicriteria Optimization and Decision Analysis (MODA) tries to find optimal solutions or solution sets for problems with multiple decision criteria. Applications are widespread: Think for instance of controlling an industrial process, minimizing the environmental impact and, at the same time, maximizing product quality and profit. Or think of the field of drug discovery where potent drug compounds are searched for with little side effects and synthesis costs.
Different kinds of computational challenges arise when solving such problems and this talk addresses a few of them. The talk will also discuss results from parametric complexity theory that reveal fundamental limits of what can be achieved by computational techniques. It will point at provable efficient algorithms that were recently discovered by the LIACS/MODA research team. Moreover, the talk will highlight a new direction of research where techniques from complex network analysis are used for analyzing problems with a large number of decision criteria.