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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 251–300 of 691 papers

TitleStatusHype
Chemi-net: a graph convolutional network for accurate drug property prediction—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Adaptive Invariance for Molecule Property Prediction—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
Chemical Property Prediction Under Experimental Biases—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating multiple models using labeled and unlabeled data—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Equivariant Neural Tangent Kernels—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Equivariant Networks for Crystal Structures—0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
3D Molecular Geometry Analysis with 2D Graphs—0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Invariance-Aware Randomized Smoothing Certificates—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models—0
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