Dr. Thomas Engel - Faculty for Chemistry and Pharmacy

- 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

- 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

- 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

- 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

- 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

- 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.

- 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 HÃ¼ckel 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

- 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.

- 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

- 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"