{"type":"ScholarlyArticle","title":"Equivariant Graph Neural Networks for Protein Interaction Modeling and Structure-Aware Molecular Design","authors":[{"name":"Animesh Animesh","slug":"animesh-animesh","given":"Animesh","family":"Animesh","affiliation":""}],"abstract":"Equivariant graph neural networks (EGNNs) have revolutionized molecular property prediction by encoding 3D structures while preserving physical symmetry. However, existing EGNNs struggle to capture fine-grained geometric features needed for tasks like understanding protein interactions, and designing molecules that modulate them are central challenges in computational drug discovery.\r\nThis thesis develops a progressive framework based on equivariant graph neural networks (EqGNNs) that respects the geometric symmetries of molecular structures.\r\nOur contributions progress along two axes: from discriminative to generative and from basic representations to physics-informed, language-conditioned generation. In this study, we propose novel methods for both discriminative (E(Q)AGNN-PPIS, GDEGAN) and generative tasks (PhysFlow). Together, these advance symmetry-aware geometric deep learning for drug discovery.","doi":"10.24963/ijcai.2026/938","url":"https://ijcai.org/proceedings/2026/938","pdf":"https://ijcai.org/proceedings/2026/0938.pdf","markdown":"https://ijcai.org/proceedings/2026/938.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/938","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":938,"pages":{"first":8319,"last":8320},"keywords":["Machine Learning: Attention models","Multidisciplinary Topics and Applications: Life sciences","Machine Learning: Geometric learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}