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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 326–350 of 691 papers

TitleStatusHype
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
Large-Scale Knowledge Integration for Enhanced Molecular Property PredictionCode0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining—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
Molecular topological deep learning for polymer property prediction—0
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
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