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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 251–300 of 691 papers

TitleStatusHype
MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
TxGemma: Efficient and Agentic LLMs for Therapeutics—0
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning—0
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification—0
Multimodal machine learning with large language embedding model for polymer property predictionCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery—0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
Transformers for molecular property prediction: Domain adaptation efficiently improves performanceCode0
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE ModelCode0
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation—0
Integrating convolutional layers and biformer network with forward-forward and backpropagation trainingCode0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
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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