Transfer learning with molecular graph convolutional networks for accurate modeling and representation of bioactivities of ligands targeting GPCRs without sufficient data. (June 2022)

• Record Type: Journal Article
• Authors: Wu, Jiansheng; Lan, Chuangchuang; Mei, Zheming; Chen, Xiaohuyan; Zhu, Yanxiang; Hu, Haifeng; Diao, Yemin
• Is Part Of: Computational biology and chemistry. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: G protein-coupled receptors (GPCRs) -- Ligand bioactivities -- Transfer learning -- Graph convolutional network; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107664
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: There are many new or potential drug targets in G protein-coupled receptors (GPCRs) without sufficient ligand associations, and it is essential and urgent to implement drug discovery targeting these GPCRs. Precise modeling and representing ligand bioactivities are essential for screening and optimizing these GPCR targeted drugs, yet insufficient samples made it difficult to achieve. Transfer learning intends to solve this by introducing rich information from related source domains with sufficient ligand training samples. In addition, ligand molecules naturally constitute a graph structure, which can be utilized by molecular graph convolutional networks to form an end-to-end multiple-level representation learning. This study proposed a novel method, TL-MGCN, using transfer learning with molecular graph convolutional networks to precisely model and represent bioactivities of ligands targeting GPCRs without sufficient data. The study tested TL-MGCN on a series of 54 representative target domain datasets which cover most human subfamilies, and 44 out of them have less than 600 ligand associations. TL-MGCN obtained an average improvement of 28.74%, 17.28%, 10.05%, 77.83%, 43.65% and 14.65% on correlation coefficient ( r 2 ) and 11.90%, 7.43%, 14.86%, 41.46%, 31.02% and 22.94% on root-mean-square error ( RMSE ) compared with the WDL-RF, transfer learning version of WDL-RF (TR-WDL-RF), attentive FP, GIN, Weave and MPNN predictors, respectively. Users have free access to the web server of TL-MGCN, along with the source codes and datasets, at http://www.noveldelta.com/TL_MGCN for academic purposes. Graphical Abstract: ga1 Highlights: Using transfer learning to solve the problem of insufficient data. Utilizing graph convolutional networks to form an end-to-end multiple-level representation. Free access to the web server of the proposed method.