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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 1–50 of 691 papers

TitleStatusHype
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
Scikit-fingerprints: easy and efficient computation of molecular fingerprints in PythonCode3
A Review of Large Language Models and Autonomous Agents in ChemistryCode3
^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network PotentialsCode3
A Python library for efficient computation of molecular fingerprintsCode3
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A SurveyCode3
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+Code3
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug DiscoveryCode3
Descriptor-based Foundation Models for Molecular Property PredictionCode2
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
M^3-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and DiscoveryCode2
Deconstructing equivariant representations in molecular systemsCode2
Generative Artificial Intelligence for Navigating Synthesizable Chemical SpaceCode2
ProtT3: Protein-to-Text Generation for Text-based Protein UnderstandingCode2
Generalizable, Fast, and Accurate DeepQSPR with fastpropCode2
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersCode2
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, GeometryCode2
Improving Molecular Properties Prediction Through Latent Space FusionCode2
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural NetworkCode2
Temporal Graph Benchmark for Machine Learning on Temporal GraphsCode2
SGFormer: Simplifying and Empowering Transformers for Large-Graph RepresentationsCode2
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
A Text-guided Protein Design FrameworkCode2
A Systematic Survey of Chemical Pre-trained ModelsCode2
Protein Representation Learning by Geometric Structure PretrainingCode2
Identity-aware Graph Neural NetworksCode2
Analyzing Learned Molecular Representations for Property PredictionCode2
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Directed Graph Grammars for Sequence-based LearningCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Wyckoff Transformer: Generation of Symmetric CrystalsCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine LearningCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic InsightsCode1
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representationsCode1
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property PredictionCode1
MAMMAL -- Molecular Aligned Multi-Modal Architecture and LanguageCode1
Multi-view biomedical foundation models for molecule-target and property predictionCode1
Publishing Neural Networks in Drug Discovery Might Compromise Training Data PrivacyCode1
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentationCode1
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityCode1
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