Chemical database

From Academic Kids

A chemical database is a database specifically designed to store chemical information. Most chemical databases store information on stable molecules. Chemical structures are traditionally represented using lines indicating chemical bonds between atoms and drawn on paper (2D). While these are ideal visual representations for the chemist, they are unsuitable for computational use and especially for search and storage.

Large chemical databases are expected to handle millions of molecules taking terabytes of physical memory.



There are two principal techniques for representing chemical structures in digital databases

  • As connection tables / adjacency matrices / lists with additional information on bond (edges) and atom attributes (nodes) eg: MDL MOL, PDB, CML
  • As string notation based on depth first or breadth first traversal eg: SMILES, SMARTS, SLN, WLN

These approaches have been refined to allow representation of stereochemical differences and charges as well as special kinds of bonding such as those seen in organo-metallic compounds. The principal advantage of a computer representation is the possibility for increased storage and fast, flexible search.


Chemists can search databases using parts of structures, parts of their IUPAC names as well as based on constraints on properties. Chemical databases are particularly different from other general purpose databases in their support for sub-structure search. This kind of search is achieved by looking for Sub-graph Isomorphism (sometimes also called a monomorphism), a branch of Graph theory. The algorithms for searching are computationally intensive, often of O (n3) or O (n4) time complexity (where n is the number of atoms involved). The intensive component of search is called atom-by-atom-searching (ABAS), in which a mapping of the search substructure atoms and bonds with the target molecule is sought. ABAS searching usually makes use of Ullman's algorithm or variations of it. Speedups are achieved by time amortization, that is, some of the search tasks are saved by precomputation. This precomputation typically involves creation of bit-strings representing presence or absence of molecular fragments. By looking at the fragments present in a search structure it is possible to eliminate the need for ABAS comparison with target molecules that do not possess the fragments that are present in the search structure. This elimination is called screening. The bit-strings used for these applications are also called structural-keys. The performance of such keys depends on the choice of the fragments used for constructing the keys and the probability of their presence in the database molecules. Another kind of key makes use of hash-codes based on fragments derived computationally. These are called 'fingerprints' although the term is sometimes used synonymously with structural-keys. The amount of memory needed to store these structural-keys and fingerprints can be reduced by 'folding', which is achieved by combining parts of the key using bitwise-operations and thereby reducing the overall length.


There is no single definition of molecular similarity, however the concept may be defined according to the application and is often described as an inverse of a measure of distance. Two molecules might be considered more similar if their difference in molecular weights is lower than when compared with others. A variety of other measures could be combined to produce a multi-variate distance measure. Distance measures are often classified into Euclidean measures and non-Euclidean measures.

Databases may be clustered into families of 'similar' molecules based on similarities. Both hierarchical and non-hierarchical clustering approaches can be applied to chemical entities with multiple attributes. These attributes or molecular properties may either be determined empirically or computationally derived descriptors. One of the most popular clustering approaches is the Jarvis-Patrick (k-nearest neighbours) algorithm.

Registration systems

Databases systems for maintaining unique records on chemical compounds are termed as Registration systems. These are often used for chemical indexing, patent systems and industrial databases.

Registration systems usually enforce uniqueness of the chemical represented in the database through the use of unique representations. By applying rules of precedence for the generation of stringified notations, one can obtain unique/'canonical' string representations such as 'canonical SMILES'. Some registration systems such as the CAS system make use of algorithms to generate unique hash codes to achieve the same objective.

Registration systems also preprocess molecules to avoid considering trivial differences such as differences in halogen ions in chemicals.


The computational representations are usually made transparent to chemists by graphical display of the data. Data entry is also simplified through the use of chemical structure editors. These editors internally convert the graphical data into computational representations.

There are also numerous algorithms for the interconversion of various formats of representation. An open-source utility for conversion is OpenBabel.

Algorithms for the conversion of IUPAC names to structure representations and vice versa are also used for extracting structural information from text. However there are difficulties due to the existence of multiple dialects of IUPAC. Work is on to establish a unique IUPAC standard (See InChI).


A good discussion group for topics on this subject is the CCL (email list).

External links

  • OpenBabel (
  • Chemical Abstracts Service (, one of the major chemical database applications
  • ACD/ChemFolder (, a PC-based software application for structure and reation databasing
  • JOELib (, a Java chemical data handling software library
  • CDK ( another Java library for chemical data handling
  • VCCLAB ( virtual computational chemistry laboratory
  • ALOGPS ( on-line calculation and comparison of several program for logP and aqueous solubility

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