Discovery of Natural Product-derived 5-HT1A Receptor Binders by Cheminfomatics Modeling of Known Binders High Throughput Screening and Experimental Validation

摘要

The human 5-hydroxytryptamine receptor subtype 1A (5-HT1A) is highly expressed in the raphe nuclei region and limbic structures; for that reason 5-HT1A had served as a promising target for treating human mood disorders and neurodegenerative diseases. We have developed binary quantitative structure-activity relationship (QSAR) models for 5-HT1A binding using data retrieved from the WOMBAT database and the k-Nearest Neighbor (kNN) machine learning method. A rigorous QSAR modeling and screening workflow had been followed, with extensive internal and external validation processes. The models’ classification accuracies to discriminate 5-HT1A binders from the non-binders were as high as 96% for the external validation. These models were employed further to mine two major natural products screening libraries, i.e. TimTec Natural Product Library (NPL) and Natural Derivatives Library (NDL). In the end five screening hits were tested by radioligand binding assays with a success rate of 40%, and two new compounds were confirmed to be binders at the µM concentration against the human 5-HT1A receptor. The combined application of rigorous QSAR modeling and model-based virtual screening presents a powerful means for profiling natural products compounds with important biomedical activities.