Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Ramil Nugmanov
{"title":"基于上下文丰富训练的分子构象增强基准测试:基于图的变压器与GNN模型","authors":"Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Ramil Nugmanov","doi":"10.1186/s13321-025-01004-5","DOIUrl":null,"url":null,"abstract":"<div><p>The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01004-5","citationCount":"0","resultStr":"{\"title\":\"Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models\",\"authors\":\"Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Ramil Nugmanov\",\"doi\":\"10.1186/s13321-025-01004-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks.</p></div>\",\"PeriodicalId\":617,\"journal\":{\"name\":\"Journal of Cheminformatics\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01004-5\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cheminformatics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s13321-025-01004-5\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cheminformatics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1186/s13321-025-01004-5","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models
The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks.
期刊介绍:
Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling.
Coverage includes, but is not limited to:
chemical information systems, software and databases, and molecular modelling,
chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases,
computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.