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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 1–50 of 691 papers

TitleStatusHype
A Review of Large Language Models and Autonomous Agents in ChemistryCode3
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+Code3
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A SurveyCode3
A Python library for efficient computation of molecular fingerprintsCode3
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug DiscoveryCode3
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
Scikit-fingerprints: easy and efficient computation of molecular fingerprints in PythonCode3
^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network PotentialsCode3
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Descriptor-based Foundation Models for Molecular Property PredictionCode2
M^3-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and DiscoveryCode2
ProtT3: Protein-to-Text Generation for Text-based Protein UnderstandingCode2
A Systematic Survey of Chemical Pre-trained ModelsCode2
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersCode2
Protein Representation Learning by Geometric Structure PretrainingCode2
Temporal Graph Benchmark for Machine Learning on Temporal GraphsCode2
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural NetworkCode2
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, GeometryCode2
SGFormer: Simplifying and Empowering Transformers for Large-Graph RepresentationsCode2
Improving Molecular Properties Prediction Through Latent Space FusionCode2
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
A Text-guided Protein Design FrameworkCode2
Analyzing Learned Molecular Representations for Property PredictionCode2
Deconstructing equivariant representations in molecular systemsCode2
Generalizable, Fast, and Accurate DeepQSPR with fastpropCode2
Generative Artificial Intelligence for Navigating Synthesizable Chemical SpaceCode2
Identity-aware Graph Neural NetworksCode2
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
ADMET property prediction through combinations of molecular fingerprintsCode1
Directed Graph Grammars for Sequence-based LearningCode1
Dual-view Molecule Pre-trainingCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
E(n) Equivariant Topological Neural NetworksCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
ChemLLM: A Chemical Large Language ModelCode1
Can Large Language Models Understand Molecules?Code1
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