title: Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.
abstract: This work proposes a new structure-activity relationship (SAR) approach to mine molecular fragments that act as structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make the predictions more reliable but also to permit clear control by the user in order to meet customized requirements. This approach has been tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, bringing to the surface much of the knowledge already collected in the literature as well as new evidence.
authors.affiliation: Department of Electronics and Information, Politecnico di Milano, Milan, Italy.
authors.forename: T
authors.initials: T
authors.lastname: Ferrari
authors.forename: D
authors.initials: D
authors.lastname: Cattaneo
authors.forename: G
authors.initials: G
authors.lastname: Gini
authors.forename: N
authors.initials: N
authors.lastname: Golbamaki Bakhtyari
authors.forename: A
authors.initials: A
authors.lastname: Manganaro
authors.forename: E
authors.initials: E
authors.lastname: Benfenati
mesh_terms: D006801:Humans; D015233:Models, Statistical; D015394:Molecular Structure; D009153:Mutagens / Q000737:chemistry*; D009930:Organic Chemicals / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D017751:Safety Management / Q000379:methods*; D014116:Toxicology / Q000379:methods*
chemical_list: D009153:Mutagens; D009930:Organic Chemicals