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

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.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

• medical and health sciences basic medicine medicinal chemistry
• natural sciences computer and information sciences artificial intelligence pattern recognition
• natural sciences mathematics applied mathematics statistics and probability
• natural sciences computer and information sciences artificial intelligence machine learning

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

• FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

• FP7-PEOPLE-2010-IEF - Marie-Curie Action: "Intra-European fellowships for career development"

Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

FP7-PEOPLE-2010-IEF
See other projects for this call

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MC-IEF - Intra-European Fellowships (IEF)

Coordinator