Modelling and predicting binding affinity of PCP-like compounds using machine learning methods

Download

index.pdf

Date

2007

Author

Erdaş, Özlem

Metadata

Show full item record

Item Usage Stats

270
views

83
downloads

Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

Subject Keywords

Computer Software.

URI

http://etd.lib.metu.edu.tr/upload/3/12608792/index.pdf
https://hdl.handle.net/11511/17102

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
Using collaboration diagrams in component oriented modelin Tuncel, Mehmet Burhan; Doğru, Ali Hikmet; Department of Computer Engineering (2006) Component Oriented Software Engineering (COSE) seems to be the future of software engineering. Currently, COSEML is the only modeling language that completely supports the COSE approach. Abstract decomposition of the system and their representing components are shown in a hierarchy diagram to support the COSE process model. In COSEML, only static modeling is supported through this single diagram. However, software is about behavior and static modeling is not sufficient to describe the system. The aim of thi...
Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
Data integration over horizontally partitioned databases in service-oriented data grids Sunercan, Hatice Kevser Sönmez; Çiçekli, Fehime Nihan; Alpdemir, Mahmut Nedim; Department of Computer Engineering (2010) Information integration over distributed and heterogeneous resources has been challenging in many terms: coping with various kinds of heterogeneity including data model, platform, access interfaces; coping with various forms of data distribution and maintenance policies, scalability, performance, security and trust, reliability and resilience, legal issues etc. It is obvious that each of these dimensions deserves a separate thread of research efforts. One particular challenge among the ones listed above tha...
Systematic component-oriented development with axiomatic design Toğay, Cengiz; Doğru, Ali Hikmet; Department of Computer Engineering (2008) In this research, component oriented development is supported with design guidance by extending the Axiomatic Design Theory for component orientation, and utilizing domain engineering and ontology mechanisms. Guidance is offered in the form of suggesting missing components and discovering incompatibilities among the candidate elements of software development, corresponding to different phases such as requirement analysis, design, and implementation. A mature domain concept is developed suggesting the availa...

Citation Formats

Ö. Erdaş, “Modelling and predicting binding affinity of PCP-like compounds using machine learning methods,” M.S. - Master of Science, Middle East Technical University, 2007.