Topic outline

  • General

    ssc3533
    Lecturer:
    		 Prof. Dr. Mohamed Noor Hasan
    Dr. Hasmerya Maarof
    Semester:
    		 Semester I 2010/11
    		model
    Synopsis
    This course introduces the application of computer methods in chemistry. Topics discussed include computer representation of chemical structures, databases in chemistry, molecular modeling, pattern recognition, optimization, regression analysis, multivariate calibration, artificial intelligence and QSAR. Applications of these methods in data analysis, structural searching, prediction of molecular properties and drug design are discussed.


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    • Topic 1

      Introduction
      Overview of computer, operating system and programming languages. Introduction to chemometric and cheminformatic methods and applications in solving chemical problems.

    • Topic 2

      Representation of chemical structures
      Fragment code, linear notation, SMILES and connection table

    • Topic 3

      Databases in Chemistry
      Chemical structure databases. Molecular similarity and structural searching.

    • Topic 4

      Molecular modelling
      Molecular mechanic (force field) and molecular orbital (ab initio and semi-empirical) methods.

    • Topic 5

      Pattern Recognition
      Supervised and unsupervised methods, Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Principal Components analysis (PCA) and Hierarchical Clustering.

    • Topic 6

      Optimization.
      Methods of optimization, simple and modified simplex

    • Topic 7

      Regression Analysis
      Simple linear regression, weighted least squares and nonlinear regression.

    • Topic 8

      Multivariate Calibration
      Multiple linear regression (MLR), principal component regression (PCR), partial least square regression (PLS).

    • Topic 9

      Artificial Intelligence
      Neural network and applications in data analysis and pattern recognition, genetic algorithms for optimization.

    • Topic 10

      QSAR
      Quantitative structure activity/property relationships, applications in predicting biological activities and physicochemical properties, drug design.