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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 351–400 of 691 papers

TitleStatusHype
Impact of SMILES Notational Inconsistencies on Chemical Language Model PerformanceCode0
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific AdaptationCode0
Molecular Joint Representation Learning via Multi-modal Information—0
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
3D Molecular Geometry Analysis with 2D Graphs—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
Adaptive Invariance for Molecule Property Prediction—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches—0
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs—0
Affinity-Aware Graph Networks—0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
AlloyBERT: Alloy Property Prediction with Large Language Models—0
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
All You Need Is Synthetic Task Augmentation—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
A molecular hypergraph convolutional network with functional group information—0
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Artificial Intelligence Enabled Material Behavior Prediction—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Atom-Motif Contrastive Transformer for Molecular Property Prediction—0
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
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