Textbook - Learning Objectives

Introduction
Representation of Chemical Compounds
Representation of Chemical Reactions
The Data
Databases/Datasources
Databases/Datasources
Calculation of Physical and Chemical Data
Calculation of Structure Descriptors
Methods for Data Analysis
Applications

1. Introduction

2. Representation of Chemical Compounds

To understand different kinds of conventional nomenclature of chemical compounds
To know how to transform a chemical structure into a language for computer representation and manipulation
To be able to represent the constitution in an unambiguous and unique manner
To learn more about connection tables and additional special notations of chemical structures
To become familiar with structure exchange formats such as Molfile and SDfile
To find out how stereochemistry can be represented in a 2D structure
To know generate 3D structures and how to represent and handle them with the computer
To be introduced to molecular surfaces and to different models for visualization
To recognize which programs can be used for the generation and visualization of molecular structures

3. Representation of Chemical Reactions

To understand how to extract knowledge from reaction information
To recognize reaction classification as an important step in learning from reaction instances
To appreciate the reaction center and its importance in reaction searching
To become familiar with basic models of chemical reactivity
To know simple approaches to quantify chemical reactivity
To be able to follow some algorithmic approaches to reaction classification
To understand the formal treatment of the stereochemistry of reactions

4. The Data

To gain a general overview on data and its pre-processing for learning
To know, in outline, the pathways for data acquisition
To understand what datasets are and how to estimate their quality
To be able to deal with outliers and redundancy
To know how to carry out scaling, mean-centering, and auto-scaling
To understand data transformations and their applicability
To know how to select an optimal subset of descriptors
To become familiar with dataset optimization techniques
To know how to validate results
To understand what training and test sets are, and how to make use of them

5. Databases/Datasources

To understand introductory basic database theory
To become familiar with the classification of chemical databases according to their data stock
To get to know various databases covering the topics of bibliographic data, physicochemical properties, and spectroscopic, crystallographic, biological, structural, reaction, and patent data
To be able to access chemical information available on the Internet

6.Structure Search Methods

To become familiar with various methods and tools for full structure recognition and the search in structural data sets.
To learn a more thorough approach to the solution of the substructure search problem.
To become familiar with the basics of chemical structure similarity, similarity measures, and different approaches exploited within the similarity search process.

7. Calculation of Physical and Chemical Data

To be able to calculate molecular properties by additivity schemes based on contributions by structural subunits
To become familiar with the estimation of thermochemical data
To understand the estimation of average drug-receptor binding energies
To become familar with the algorithm for charge calculation by partial equalization of orbital electronegativity (PEOE) and by a modified Hückel Molecular Orbital method
To appreciate residual electronegativity as a measure of the inductive effect
To follow a simple scheme for calculating the polarizability effect
To know how linear equations can be used for calculation of enthalpies of gas-phase reactions
To understand the basic concepts of force field-calculations
To see the contributions to the molecular mechanics potential energy function and their mathematical representation
To get an overview of the currently available software and implementations with their strengths, weaknesses, and application areas
To understand the importance of investigating the dynamical behavior of molecules
To have an overview of the algorithms and basic concepts used to perform molecular dynamics simulations
To consider exemplary state-of-the-art applications of MD simulations
To become familiar with the different quantum mechanical methods
To know which properties can be derived from quantum mechanical methods
To ponder on the future of quantum mechanics in chemoinformatics

8. Calculation of Structure Descriptors

To understand what structure descriptors are.
To know what QSAR and QSPR are, and the steps in QSAR/QSPR.
To find out how to distinguish between the different kinds of molecular descriptors.
To understand the recommendations for structure descriptors in order to be able to apply them in QSAR or drug design in conjunction with statistical or machine learning techniques.
To become familiar with the properties of these descriptors.
To know which are the frequently used descriptors.

9. Methods for Data Analysis

To understand the machine learning process and learning concepts
To become familiar with the structure and task of decision trees
To gain insight into chemometric methods such as correlation analysis, Multiple Linear Regression Analysis, Principal Component Analysis, Principal Component Regression, and Partial Least Squares regression/Projection to Latent Variables
To understand neural networks, especially Kohonen, counterpropagation and backpropagation networks, and their applications
To know about fuzzy sets and fuzzy logic
To become familiar with genetic algorithms and their application for descriptor selection
To understand data mining and data mining tasks
To understand visual data mining and information visualization techniques
To appreciate the architecture and tasks of expert systems and examples of expert systems in chemistry

10. Applications

To understand how to derive a quantitative relationship between property and structure
To become familiar with the application of the basic principles of the model building process by means of calculating log P and log S values
To acquire an overview of methods and examples of some pitfalls in modeling log P, log S, and the toxic effects of compounds
To identify the main methods and tools available for the computer prediction of spectra from the molecular structure, and for automatic structure elucidation from spectral data
To realize that a proper representation of the molecular structure is crucial for the pre-diction of spectra
To recognize the main approaches for structure representation in the context of struc-ture-spectra correlations
To be able to define reaction planning, reaction prediction, and synthesis design
To know how to acquire knowledge from reaction databases
To understand reaction simulation systems
To become familiar with a knowledge-based reaction prediction system
To appreciate the different levels of the evaluation of chemical reactions
To know how reaction sequences are modeled
To understand kinetic modeling of chemical reactions
To become familiar with biochemical pathways
To recognize the different levels of representation of biochemical reactions
To understand metabolic reaction networks
To know the principles of retrosynthetic analysis
To understand the disconnection approach
To become familiar with synthesis design systems
Developing a suitable synthesis strategy for a target compound by searching for synthesis precursors, starting materials and synthesis reactions
To become familiar with the drug discovery process
To find out what a lead structure is
To appreciate the impact of chemoinformatics on the drug discovery process
To understand the "similar property" principle
To know what virtual screening is
To become familiar with Lipinski's "Rule of Five"