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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 226–250 of 691 papers

TitleStatusHype
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 Prediction—0
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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