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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 151–175 of 691 papers

TitleStatusHype
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules—0
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property PredictionCode0
Deconstructing equivariant representations in molecular systemsCode2
Molecular topological deep learning for polymer property prediction—0
Generative Artificial Intelligence for Navigating Synthesizable Chemical SpaceCode2
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
Task Addition in Multi-Task Learning by Geometrical Alignment—0
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries—0
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction—0
Self-supervised learning for crystal property prediction via denoising—0
Do Graph Neural Networks Work for High Entropy Alloys?Code0
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure UnderstandingCode1
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
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