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3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information

https://doi.org/10.1007/s10044-024-01287-8

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology

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Spatial Graph Convolutional Networks

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GPT-3: Its Nature, Scope, Limits, and Consequences

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Deep learning methods for molecular representation and property prediction

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OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction

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Molecular fingerprint similarity search in virtual screening

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Advances in neural information processing systems 7

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Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

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ZINC20─A Free Ultralarge-Scale Chemical Database for Ligand Discovery

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Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

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ReLMole: Molecular Representation Learning Based on Two-Level Graph Similarities

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Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction

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Analyzing Learned Molecular Representations for Property Prediction

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libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications

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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

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Reoptimization of MDL Keys for Use in Drug Discovery

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Extended-Connectivity Fingerprints

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Geometry-enhanced molecular representation learning for property prediction

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Molecular contrastive learning of representations via graph neural networks

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MoleculeNet: a benchmark for molecular machine learning

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OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

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DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges

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Multiphysical graph neural network (MP-GNN) for COVID-19 drug design

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DeepPurpose: a deep learning library for drug–target interaction prediction

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Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

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PubChem: a public information system for analyzing bioactivities of small molecules

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Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties

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GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

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SMILES-BERT

inferredcites →https://doi.org/10.1145/3442381.3449822openalex-snapshot

SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

inferredcites →https://doi.org/10.1145/3447548.3467186openalex-snapshot

MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph

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Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

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Uni-Mol: A Universal 3D Molecular Representation Learning Framework

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AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

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Neural Message Passing for Quantum Chemistry

inferredcites →https://doi.org/10.48550/arxiv.1706.08566openalex-snapshot

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

inferredcites →https://doi.org/10.48550/arxiv.1712.02034openalex-snapshot

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

inferredcites →https://doi.org/10.48550/arxiv.1806.09206openalex-snapshot

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

inferredcites →https://doi.org/10.48550/arxiv.1905.12265openalex-snapshot

Strategies for Pre-training Graph Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.2010.09885openalex-snapshot

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

inferredcites →https://doi.org/10.48550/arxiv.2010.13902openalex-snapshot

Graph Contrastive Learning with Augmentations

inferredcites →https://doi.org/10.48550/arxiv.2110.04126openalex-snapshot

3D Infomax improves GNNs for Molecular Property Prediction

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Pre-training Molecular Graph Representation with 3D Geometry

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Polymer informatics: Integrating data‐driven strategies, advanced machine learning, and automated synthesis for next‐generation polymer design

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TMolNet: a task-aware multimodal neural network for molecular property prediction

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Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches

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Physically valid adversarial examples for 3D molecular prediction

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Graph residual based method for molecular property prediction

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ConBiMamba-driven multimodal contrastive learning with dual heterogeneous cross-attention for molecular property prediction

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HSBNet: Fusing semantics and anisotropic thermal diffusion fields for boundary-aware point cloud segmentation

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Multiscale-Information-Embedded Universal Toxicity Prediction Framework

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Recent advances in molecular representation methods and their applications in scaffold hopping

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Bond-Aware Molecular Graph Learning With Multi-Graph Interleaved Message Passing

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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling

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TGF-M: Topology-augmented geometric features enhance molecular property prediction

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PairReg: A method for enhancing the learning of molecular structure representation in equivariant graph neural networks

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Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey

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Integrated 3D Molecular Visualization and Property Prediction Using SMILES-Based Cheminformatics and Machine Learning

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Chemical Bonding and Molecular Structure: Foundations of Chemistry

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