{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-modality-alignment-network-for","title":"Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval","arxiv_id":null,"date":"2023-03-08","proceeding":"IEEE Transactions on Artificial Intelligence 2023 3","authors":["Wenyu Zhao","Dong Zhou","Buqing Cao","Kai Zhang","Jinjun Chen"],"abstract":"The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this non-trivial problem relies on graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However, there exist the following issues: 1) the cross-modal attention mechanism is only in favor of text representations and can not provide helpful information for molecule representations. 2) the GCN-based molecule encoder ignores edge features and the importance of various substructures of a molecule. 3) the retrieval learning loss function is rather simplistic. This paper further investigates the Text2Mol problem and proposes a novel Adversarial Modality Alignment Network (AMAN)-based method to sufficiently learn both description and molecule information. Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations. Then an adversarial network is used to align these modalities interactively. Meanwhile, a triplet loss function is leveraged to perform retrieval learning and further enhance the modality alignment. Experiments on the ChEBI-20 dataset show the effectiveness of our AMAN method compared with baselines.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10063974","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10063974","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adversarial-modality-alignment-network-for","repo_url":"https://github.com/NicoleBonnie/AMAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":null,"task_name":"Triplet"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"graph-transformer","method_name":"Graph Transformer"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"lapeigen","method_name":"LapEigen"},{"method_slug":"laplacian-pe","method_name":"Laplacian PE"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"triplet-loss","method_name":"Triplet Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-modal-retrieval-on-chebi-20","task":"Cross-Modal Retrieval","dataset":"ChEBI-20","model":"AMAN","rank_in_archive_order":6,"of":9,"metrics":{"Hits@1":"49.4","Hits@10":"92.1","Mean Rank":"16.01","Test MRR":"64.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}