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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 76–100 of 691 papers

TitleStatusHype
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
MoMa: A Modular Deep Learning Framework for Material Property Prediction—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
MatterChat: A Multi-Modal LLM for Material Science—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction—0
Towards Data-Efficient Pretraining for Atomic Property PredictionCode0
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic ScreeningCode0
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization—0
ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine LearningCode1
Evaluating multiple models using labeled and unlabeled data—0
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