From: Predicting a small molecule-kinase interaction map: A machine learning approach

Short-hand | Full Name | # Features | Feature Type |
---|---|---|---|

PT
| Primary Target | 1 | nominal |

Summary
Determine for which kinase(s) an inhibitor shows a preferred binding | |||

MS
| 2D Molecular Structure | 1 | nominal |

Summary
Clustering of inhibitors due to their 2D structure Cluster_{1} = {SB202190, SB203580}Cluster_{2} = {CI-1033, EKB-569, MLN-518}Cluster_{3} = {Staurosporine; LY-333531}Cluster_{4} = {Roscovitine; Flavopiridol; BIRB-796}Cluster_{5} = {SU 11248; BAY-43-9006; ZD-6474; Gleevec,GW-2016, Iressa, Tarceva, V X-745, V atalanib, SP600125}
| |||

FTs
| Free Trees | 78 | numeric |

Summary
Determine frequently occurring acyclic substructures as structural features | |||

KNN
| KNN clustering | 20 | numeric |

Summary
Detect each inhibitor's k nearest neighbors
| |||

CF
| Chemical Features | 15 | numeric |

Summary
Calculate various chemical features with JOELib2 | |||

GF
| Geometric Features | 5 | numeric |

Summary
Calculate various geometric features | |||

P
| Pharmacophores | 50 | numeric |

Summary
Calculate 3-point pharmacophores |