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 |