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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 51–100 of 691 papers

TitleStatusHype
ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure UnderstandingCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
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
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property PredictionCode1
TAGMol: Target-Aware Gradient-guided Molecule GenerationCode1
E(n) Equivariant Topological Neural NetworksCode1
Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and PoseCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
Global Concept Explanations for Graphs by Contrastive LearningCode1
Kermut: Composite kernel regression for protein variant effectsCode1
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024Code1
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised LearningCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
ChemLLM: A Chemical Large Language ModelCode1
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation NetworksCode1
Can Large Language Models Understand Molecules?Code1
Molecular Hypergraph Neural NetworksCode1
Graph Transformers for Large GraphsCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
Removing Biases from Molecular Representations via Information MaximizationCode1
Multimodal Foundation Models for Material Property Prediction and DiscoveryCode1
Improving Self-supervised Molecular Representation Learning using Persistent HomologyCode1
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionCode1
UniMAP: Universal SMILES-Graph Representation LearningCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Lo-Hi: Practical ML Drug Discovery BenchmarkCode1
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
On the Stability of Expressive Positional Encodings for GraphsCode1
AstroCLIP: A Cross-Modal Foundation Model for GalaxiesCode1
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingCode1
ADMET property prediction through combinations of molecular fingerprintsCode1
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational GraphsCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture SearchCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
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