Predicting subtype selectivity of dopamine receptor ligands with three‐dimensional biologically relevant spectrum
Zhengkun Kuang, Shiyu Feng, Ben K. S. Hu, Dong Wang, Songbing He, Kong De · Chemical Biology & Drug Design · 2016
We applied a novel molecular descriptor, three‐dimensional biologically relevant spectrum (BRS‐3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS‐3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc‐PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1‐D2, D1‐D3, D2‐D3, D2‐D4, and D3‐D4. The models’ 10‐fold cross‐validation‐squared correlation coefficient (Q2, for training sets) and determination coefficient (R2, for test sets) were in the range of 0.5–0.7 and 0.6–0.8, respectively. Then, four pair‐wise (D1‐D2, D2‐D3, D2‐D4, and D3‐D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS‐3D and classical descriptors. The results showed that BRS‐3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS‐3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS‐3D in the prediction of DR subtype‐selective ligands.