Data mining algorithms

Library Design Based on Special Data Mining Algorithms... 

Design based on special data mining algorithms... 

LIBRARY DESIGN BASED ON SPECIAL DATA MINING ALGORITHMS... 

Knowledge-based data mining algorithms used for correlation of molecular properties with specific activities play an increasingly significant role in modern strategies of chemical library design as relatively inexpensive, yet comprehensive tools. The ability to identify compounds with the desired... 

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

In this work, we attempted to solve this problem using special cheminformatics tools and data-mining algorithms. 

Figure 2.1 The evolution of the drug-likeness concept. Drug-likeness evolved from empirical rules such as Lipinski s rule of 5 through more sophisticated data mining algorithms into utilization of preclinical profiling and safety pharmacology data [3].

Christopher C. Fischer, Gerbrand Ceder, Kevin J. Tibbetts, and Dane Morgan develop a data-mining algorithm to look at crystal structures. 

Fischer, Christopher C., Kevin J. Tibbetts, Dane Morgan, and Gerbrand Ceder. Predicting Crystal Structure by Merging Data Mining with Quantum Mechanics. Nature Materials 5 (2006) 641-646. The researchers developed a data-mining algorithm in 2006 to look at crystal structures. 

Additionally, as errors can easily occur in databases, it cannot be assumed that the data they contain are entirely correct. Even after data cleaning - a process to remove obvious errors and duplicates - there may be inherent errors or mis-classification in the data being collected, particularly if there is subjectivity involved in the measurement that is used. Furthermore, in large, constantly changing databases, there must be rules in place for the data mining algorithm to capture the most current data. 

Kretschmann, E., W. Fleischmann, and R. Apweiler. 2001. Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT. Bioinformatics 17 920-6. 

Hauben, M., Horn, S., Sc Reich, L. (2007). Potential use of data-mining algorithms for the detection of surprise adverse drug reactions. Drug Safety An International Journal of Medical Toxicology and Drug Eocperience, 30(2), 143—155. 

The Decision tree method is widely used for classification and regression. A decision tree is a flow-chart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and leaf nodes represent classes or class distributions. In order to classify an unknown sample, the attribute values of the sample are tested according to the decision tree starting from the root until one of the leaves. To build decision trees, a data mining algorithm recursively inspects the available data set to find decisions that optimally split the data into distinguished subsets. An important property of this technique is that its functioning is easily understood. 

---