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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 331–340 of 691 papers

TitleStatusHype
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
Large-Scale Knowledge Integration for Enhanced Molecular Property PredictionCode0
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
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