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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 201–250 of 691 papers

TitleStatusHype
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
A Review of Large Language Models and Autonomous Agents in ChemistryCode3
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network PotentialsCode3
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
Learning Molecular Representation in a CellCode1
Towards Neural Scaling Laws for Foundation Models on Temporal GraphsCode1
Learning Multi-view Molecular Representations with Structured and Unstructured KnowledgeCode0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Equivariant Neural Tangent Kernels—0
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property PredictionCode1
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
TAGMol: Target-Aware Gradient-guided Molecule GenerationCode1
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
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
E(n) Equivariant Topological Neural NetworksCode1
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and PoseCode1
ProtT3: Protein-to-Text Generation for Text-based Protein UnderstandingCode2
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMACode0
Global Concept Explanations for Graphs by Contrastive LearningCode1
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Kermut: Composite kernel regression for protein variant effectsCode1
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning—0
Generalizable, Fast, and Accurate DeepQSPR with fastpropCode2
AlloyBERT: Alloy Property Prediction with Large Language Models—0
A Python library for efficient computation of molecular fingerprintsCode3
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Contextualized Messages Boost Graph RepresentationsCode0
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged ZeolitesCode0
Complete and Efficient Graph Transformers for Crystal Material Property PredictionCode0
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction—0
Representing Molecules as Random Walks Over Interpretable Grammars—0
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints—0
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