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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 151–160 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
Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property PredictionCode0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
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
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