Use of Machine Learning in Chemoinformatics

Irene KouskoumvekakiAssociate Professor

December 12th, 2012Biological Sequence Analysis course

2 CBS, Department of Systems Biology

Major Aspects of Chemoinformatics

•Databases: Development of databases for storage and retrieval of small molecule structures and their properties.

•Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data.

•Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

Machine Learning

Machine learning classifiers

Clustering: Self Organizing Maps

Distinguishing molecules of different biological activities and finding a new lead structure

Machine Learning

Molecular

StructuresProperties

Molecular Descriptors

Virtual Screening

Clustering

Classification

Different descriptor types

• Simple feature counts (such as number of rotatable bonds or molecular weight)

• Fragmental descriptors which indicate the presence or absence (or count) of groups of atoms and substructures

• Physicochemical properties (density, solubility, vdWaals volume)

• Topological indices (size, branching, overall shape)

Major Aspects of Chemoinformatics

•Databases: Development of databases for storage and retrieval of small molecule structures and their properties.

•Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data.

•Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

In QSAR models structural parameters (descriptors) are fitted to experimental data for biological activity (or another given property, P)

Quantitative Structure-Activity Relationships (QSAR)

Prediction of Solubility, ADME & Toxicity

hERG Classification with SVM

Evaluation of the data set

Performance of SVM

Virtual screening

Similarity Search

• Similar Property Principle – Molecules having similar structures and properties are expected to exhibit similar biological activity.

• Thus, molecules that are located closely together in the chemical space are often considered to be functionally related.

Fingerprints-based Similarity Search

– widely used similarity search tool– consists of descriptors encoded as bit strings– Bit strings of query and database are compared using

similarity metric such as Tanimoto coefficient

MACCS fingerprints: 166 structural keys

that answer questions of the type:

• Is there a ring of size 4?

• Is at least one F, Br, Cl, or I present?

where the answer is either

TRUE (1) or FALSE (0)

Tanimoto Similarity

109 90.9

or 90% similarity

Similarity Search

Questions?

Molecular editors and viewers

http://www.chemaxon.com/products/marvin/

http://jmol.sourceforge.net/

Molecular editors and viewers

Format conversion

http://cactus.nci.nih.gov/translate/

Use of Machine Learning in Chemoinformatics

Documents

Transcript of Use of Machine Learning in Chemoinformatics

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