A QSAR model of HERG binding using a large, diverse, and internally consistent training set.
Over the past decade, the pharmaceutical industry has begun to address an addition to ADME/Tox profiling--the ability of a compound to bind to and inhibit the human ether-a-go-go-related gene (hERG)-encoded cardiac potassium channel. With the compilation of a large and diverse set of compounds measured in a single, consistent hERG channel inhibition assay, we recognized a unique opportunity to attempt to construct predictive QSAR models. Early efforts with classification models built from this training set were very encouraging. Here, we report a systematic evaluation of regression models based on neural network ensembles in conjunction with a variety of structure representations and feature selection algorithms. The combination of these modeling techniques (neural networks to capture non-linear relationships in the data, feature selection to prevent over-fitting, and aggregation to minimize model instability) was found to produce models with very good internal cross-validation statistics and good predictivity on external data.
Seierstad
Mark
M
Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Row, San Diego, CA 92121, USA. mseierst@prdus.jnj.com
Agrafiotis
Dimitris K
DK
D000465:Algorithms; D003198:Computer Simulation; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D020128:Inhibitory Concentration 50; D015233:Models, Statistical; D016571:Neural Networks, Computer; D026902:Potassium Channel Blockers / Q000737:chemistry* / Q000494:pharmacology*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human; D026902:Potassium Channel Blockers