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25 April 2024 |
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Article overview
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A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases | | Song Li
; Song Ke
; Chenxing Yang
; Jun Chen
; Yi Xiong
; Lirong Zheng
; Hao Liu
; Liang Hong
; | | Date: |
23 Jul 2022 | | Abstract: | Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic
target for the treatment of uterine diseases. To date, several GnRH1R
antagonists are available in clinical investigation without satisfying multiple
property constraints. To fill this gap, we aim to develop a deep learning-based
framework to facilitate the effective and efficient discovery of a new orally
active small-molecule drug targeting GnRH1R with desirable properties. In the
present work, a ligand-and-structure combined model, namely LS-MolGen, was
firstly proposed for molecular generation by fully utilizing the information on
the known active compounds and the structure of the target protein, which was
demonstrated by its superior performance than ligand- or structure-based
methods separately. Then, a in silico screening including activity prediction,
ADMET evaluation, molecular docking and FEP calculation was conducted, where
~30,000 generated novel molecules were narrowed down to 8 for experimental
synthesis and validation. In vitro and in vivo experiments showed that three of
them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM,
compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and
compound 5 performed well in fundamental PK properties, such as half-life, oral
bioavailability, and PPB, etc. We believed that the proposed
ligand-and-structure combined molecular generative model and the whole
computer-aided workflow can potentially be extended to similar tasks for de
novo drug design or lead optimization. | | Source: | arXiv, 2207.11547 | | Services: | Forum | Review | PDF | Favorites | |
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