
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
preprocessing 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, meancentering,
and autoscaling

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 drugreceptor
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 gasphase reactions

To understand the basic concepts of force fieldcalculations

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 stateoftheart 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 prediction of spectra

To recognize the main approaches for structure
representation in the context of structurespectra 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 knowledgebased 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"
