{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["24(1)"],"submitter":["Du W"],"funding":["Jiangsu Natural Science Foundation","Natural Science Foundation of China","Anhui Science Foundation for Distinguished Young Scholars","Project of Stable Support for Youth Team in Basic Research Field, CAS"],"pubmed_abstract":["The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledg"],"journal":["Briefings in bioinformatics"],"pagination":["bbac560"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9851338"],"repository":["biostudies-literature"],"pubmed_title":["Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers."],"pmcid":["PMC9851338"],"pubmed_authors":["Yang X","Jiang J","Huo Y","Du W","Wu D","Chen X","Bao C","Wang Y","Zhang B","Ma F"],"additional_accession":[]},"is_claimable":false,"name":"Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.","description":"The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledg","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2026-03-17T15:25:02.484Z","creation":"2025-04-05T22:50:10.93Z"},"accession":"S-EPMC9851338","cross_references":{"pubmed":["36528804"],"doi":["10.1093/bib/bbac560"]}}