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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 251–275 of 691 papers

TitleStatusHype
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
Chemical Property Prediction Under Experimental Biases—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating multiple models using labeled and unlabeled data—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Heat Kernel Goes Topological—0
Equivariant Neural Tangent Kernels—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
Equivariant Networks for Crystal Structures—0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning—0
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