Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Detection of Local Interactions in the Cascade Model
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Datascape Survey Using the Cascade Model
DS '02 Proceedings of the 5th International Conference on Discovery Science
A correlation-based approach to attribute selection in chemical graph mining
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Pharmacophore knowledge refinement method in the chemical structure space
DS'07 Proceedings of the 10th international conference on Discovery science
Active mining project: overview
AM'03 Proceedings of the Second international conference on Active Mining
| Hi-index | 0.00 |
Active responses from analysts play an essential role in obtaining insights into structure activity relationships (SAR) from drug data. Experts often think of hypotheses, and they want to reflect these ideas in the attribute generation and selection process. We analyzed the SAR of dopamine agonists and antagonists using the cascade model. The presence or absence of linear fragments in molecules constitutes the core attribute in the mining. In this paper, we generated attributes indicating the presence of hydrogen bonds from 3D coordinates of molecules. Various improvements in the fragment expressions are also introduced following the suggestions of chemists. Attribute selection from the generated fragments is another key step in mining. Close interactions between chemists and system developers have enabled spiral mining, in which the analysis results are incorporated into the development of new functions in the mining system. All these factors are necessary for success in SAR mining.