Unified Molecular Modeling via Modality Blending

• URL: http://arxiv.org/abs/2307.06235v1
• Date: Wed, 12 Jul 2023 15:27:06 GMT
• Title: Unified Molecular Modeling via Modality Blending
• Authors: Qiying Yu, Yudi Zhang, Yuyan Ni, Shikun Feng, Yanyan Lan, Hao Zhou, Jingjing Liu
• Abstract summary: 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.
• Score: 35.16755562674055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised molecular representation learning is critical for molecule-based tasks such as AI-assisted drug discovery. Recent studies consider leveraging both 2D and 3D information for representation learning, with straightforward alignment strategies that treat each modality separately. In this work, we introduce a novel "blend-then-predict" self-supervised learning method (MoleBLEND), which blends atom relations from different modalities into one unified relation matrix for encoding, then recovers modality-specific information for both 2D and 3D structures. By treating atom relationships as anchors, seemingly dissimilar 2D and 3D manifolds are aligned and integrated at fine-grained relation-level organically. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (inter-modal prediction) and mask-then-predict (intra-modal prediction) objectives into a single cohesive blend-then-predict framework.

Related papers

• Benchmark on Drug Target Interaction Modeling from a Structure Perspective [48.60648369785105]
  Drug-target interaction prediction is crucial to drug discovery and design. Recent methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets. We conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms.
  arXiv  Detail & Related papers  (2024-07-04T16:56:59Z)
• Medication Recommendation via Dual Molecular Modalities and Multi-Substructure Enhancement [5.027701313370709]
  Existing works based on molecular knowledge neglect the 3D geometric structure of molecules. We propose BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices. We use deep learning networks to construct a pretraining method that acquires 2D and 3D molecular structure representations and substructure representations.
  arXiv  Detail & Related papers  (2024-05-30T07:13:08Z)
• Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation [42.08917809689811]
  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.
  arXiv  Detail & Related papers  (2024-04-23T12:35:44Z)
• I$^2$MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation [147.2183428328396]
  We introduce a general Inter- and Intra-modal Mutual Distillation (I$2$MD) framework. In I$2$MD, we first re-formulate the cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the Intra-modal Mutual Distillation (IMD) strategy.
  arXiv  Detail & Related papers  (2023-10-24T07:22:17Z)
• 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)
• 3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information [1.1777304970289215]
  3D-Mol is a novel approach designed for more accurate spatial structure representation. It deconstructs molecules into three hierarchical graphs to better extract geometric information. We compare 3D-Mol with various state-of-the-art baselines on 7 benchmarks and demonstrate our outstanding performance.
  arXiv  Detail & Related papers  (2023-09-28T10:05:37Z)
• Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction [4.598522704308923]
  Geometry-aware line graph transformer (Galformer) pre-training is a novel self-supervised learning framework. Galformer consistently outperforms all baselines on both classification and regression tasks.
  arXiv  Detail & Related papers  (2023-09-01T14:20:48Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation [130.08432609780374]
  In 3D action recognition, there exists rich complementary information between skeleton modalities. We propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs. Our approach outperforms existing self-supervised methods and sets a series of new records.
  arXiv  Detail & Related papers  (2022-08-26T06:06:09Z)

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.