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 has 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 are 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 Library 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.
Keywords: 5-HT1A receptor, external validation, high-throughput screening, natural products, bioactivity profiling, QSAR.
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