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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 241–250 of 691 papers

TitleStatusHype
Generalizable, Fast, and Accurate DeepQSPR with fastpropCode2
AlloyBERT: Alloy Property Prediction with Large Language Models—0
A Python library for efficient computation of molecular fingerprintsCode3
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Contextualized Messages Boost Graph RepresentationsCode0
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged ZeolitesCode0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction—0
Representing Molecules as Random Walks Over Interpretable Grammars—0
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints—0
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