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Molecular Property Prediction
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Molecular property prediction is the task of predicting the properties of a molecule from its structure.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Descriptor-based Foundation Models for Molecular Property Prediction
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Graph Neural Networks in Modern AI-aided Drug Discovery
Recent Developments in GNNs for Drug Discovery
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
All You Need Is Synthetic Task Augmentation
Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Learning Hierarchical Interaction for Accurate Molecular Property Prediction
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery
Transformers for molecular property prediction: Domain adaptation efficiently improves performance
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Knowledge-aware contrastive heterogeneous molecular graph learning
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction Networks
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability
Can Molecular Evolution Mechanism Enhance Molecular Representation?
Evaluating multiple models using labeled and unlabeled data
Molecular Graph Contrastive Learning with Line Graph
Dual-Modality Representation Learning for Molecular Property Prediction
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
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