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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 251–275 of 691 papers

TitleStatusHype
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A SurveyCode3
Material Microstructure Design Using VAE-Regression with Multimodal Prior—0
Molecule Design by Latent Prompt Transformer—0
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024Code1
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised LearningCode1
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey—0
ChemLLM: A Chemical Large Language ModelCode1
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersCode2
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation NetworksCode1
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific AdaptationCode0
Accelerating Material Property Prediction using Generically Complete Isometry InvariantsCode0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language ModelsCode0
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studyCode0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, GeometryCode2
Can Large Language Models Understand Molecules?Code1
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
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