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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 451–500 of 691 papers

TitleStatusHype
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Equivariant Networks for Crystal Structures—0
Equivariant Neural Tangent Kernels—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Evaluating multiple models using labeled and unlabeled data—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Explanatory Masks for Neural Network Interpretability—0
Extracting Material Property Measurement Data from Scientific Articles—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
G^3: Representation Learning and Generation for Geometric Graphs—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
Gaussian Process Molecule Property Prediction with FlowMO—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
Generative Deep Learning Framework for Inverse Design of Fuels—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
Graph Convolution: A High-Order and Adaptive Approach—0
Graph Convolutional Neural Networks for Polymers Property Prediction—0
Graph Generative Pre-trained Transformer—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Graph Networks with Spectral Message Passing—0
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