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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 276–300 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
MatterChat: A Multi-Modal LLM for Material Science—0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction—0
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic ScreeningCode0
Towards Data-Efficient Pretraining for Atomic Property PredictionCode0
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
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
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
Evaluating multiple models using labeled and unlabeled data—0
Molecular Graph Contrastive Learning with Line GraphCode0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Graph Generative Pre-trained Transformer—0
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