Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities

An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design. Graphic abstract

Automated summary

The elastic network model, as a novel tool, shows high accuracy and speed in predicting the activity of molecules with different chemical structures but common biological activity, making it a promising tool for industrial applications such as drug and material design.