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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 351–400 of 691 papers

TitleStatusHype
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches—0
Out-of-distribution materials property prediction using adversarial learning based fine-tuning—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs—0
Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning—0
Distribution Learning for Molecular Regression—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Graph Residual based Method for Molecular Property Prediction—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Guided Latent Slot Diffusion for Object-Centric Learning—0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?Code0
Token-Mol 1.0: Tokenized drug design with large language model—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction—0
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Equivariant Neural Tangent Kernels—0
Topological Neural Networks go Persistent, Equivariant, and Continuous—0
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property PredictionCode0
A General Approach for Determining Applicability Domain of Machine Learning Models—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMACode0
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
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