SYMPATI

Symbolic Pattern Recognition in Drug Design - Statistical Models and Scientific Insight

Coordinatore	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH    more  Organization address address: Raemistrasse 101 city: ZUERICH postcode: 8092 contact info Titolo: Prof. Nome: Gisbert Cognome: Schneider Email: send email Telefono: +41 44 633 73 27 Fax: +41 44 633 13 79
Nazionalità Coordinatore	Switzerland [CH]
Totale costo	170˙401 €
EC contributo	170˙401 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2010-IEF
Funding Scheme	MC-IEF
Anno di inizio	2011
Periodo (anno-mese-giorno)	2011-10-01   -   2013-09-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH  Organization address address: Raemistrasse 101 city: ZUERICH postcode: 8092 contact info Titolo: Prof. Nome: Gisbert Cognome: Schneider Email: send email Telefono: +41 44 633 73 27 Fax: +41 44 633 13 79	CH (ZUERICH)	coordinator	170˙401.60

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 Obiettivo del progetto (Objective)

'Profound understanding of the relationship between molecular structure and biological activity is an essential prerequisite for rational drug design. Due to the inherent non-linearity of these relationships, ordinary regression is often inadequate, and black-box machine learning approaches offer limited, if any, interpretability. As a solution, we propose to use symbolic regression, in conjunction with a unique approach to feature selection, to establish quantitative structure-activity relationships (QSAR) that are succinct, non-linear, analytical, and interpretable. Symbolic regression is a stochastic optimization technique based on principles of evolution that searches the space of analytical expressions for equations that describe the investigated data. In other words, symbolic regression does not only fit the coefficients of an equation, but also the form of the equation itself. Our particular concept has not been used in QSAR before. We will combine theoretical investigations of the method with practical applications. Large combinatorial libraries will be analyzed to obtain validated QSAR models that are immediately and intuitively interpretable. Taking trypsin inhibition as an example, we will design, synthesize, and test new inhibitors suggested by our models. Our interdisciplinary project will contribute to European excellency in basic and applied pharmaceutical and medicinal chemistry, in line with health as a top priority of the seventh framework programme.'