Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation

• URL: http://arxiv.org/abs/2404.16880v1
• Date: Tue, 23 Apr 2024 12:35:44 GMT
• Title: Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
• Authors: Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong,
• Abstract summary: We propose Atomas, a multi-modal molecular representation learning framework to jointly learn representations from SMILES string and text. In the retrieval task, Atomas exhibits robust generalization ability and outperforms the baseline by 30.8% of recall@1 on average. In the generation task, Atomas achieves state-of-the-art results in both molecule captioning task and molecule generation task.
• Score: 42.08917809689811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields, including drug discovery and materials science. Existing studies adopt a global alignment approach to learn the knowledge from different modalities. These global alignment approaches fail to capture fine-grained information, such as molecular fragments and their corresponding textual description, which is crucial for downstream tasks. Furthermore, it is incapable to model such information using a similar global alignment strategy due to data scarcity of paired local part annotated data from existing datasets. In this paper, we propose Atomas, a multi-modal molecular representation learning framework to jointly learn representations from SMILES string and text. We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels. Additionally, Atomas's end-to-end training framework incorporates the tasks of understanding and generating molecule, thereby supporting a wider range of downstream tasks. In the retrieval task, Atomas exhibits robust generalization ability and outperforms the baseline by 30.8% of recall@1 on average. In the generation task, Atomas achieves state-of-the-art results in both molecule captioning task and molecule generation task. Moreover, the visualization of the Hierarchical Adaptive Alignment model further confirms the chemical significance of our approach. Our codes can be found at https://anonymous.4open.science/r/Atomas-03C3.

Related papers

• Learning Molecular Representation in a Cell [18.170650265987792]
  We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods.
  arXiv  Detail & Related papers  (2024-06-17T19:48:42Z)
• UniIF: Unified Molecule Inverse Folding [67.60267592514381]
  We propose a unified model UniIF for inverse folding of all molecules. Our proposed method surpasses state-of-the-art methods on all tasks.
  arXiv  Detail & Related papers  (2024-05-29T10:26:16Z)
• Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks [44.934084652800976]
  We introduce the first MoleculAR Conformer Ensemble Learning benchmark to thoroughly evaluate the potential of learning on conformer ensembles. Our findings reveal that direct learning from an conformer space can improve performance on a variety of tasks and models.
  arXiv  Detail & Related papers  (2023-09-29T20:06:46Z)
• Unified Molecular Modeling via Modality Blending [35.16755562674055]
  We introduce a novel "blend-then-predict" self-supervised learning method (MoleBLEND) MoleBLEND blends atom relations from different modalities into one unified relation for matrix encoding, then recovers modality-specific information for both 2D and 3D structures. Experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D benchmarks.
  arXiv  Detail & Related papers  (2023-07-12T15:27:06Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Generation of 3D Molecules in Pockets via Language Model [0.0]
  Generative models for molecules based on sequential line notation (e.g. SMILES) or graph representation have attracted an increasing interest in the field of structure-based drug design. We introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology.
  arXiv  Detail & Related papers  (2023-05-17T11:31:06Z)
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [63.60376252491507]
  We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
  arXiv  Detail & Related papers  (2022-09-12T00:56:57Z)
• ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction [61.33144688400446]
  We propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning.
  arXiv  Detail & Related papers  (2020-07-07T04:22:39Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.