Molecular Property Prediction
124 papers with code • 18 benchmarks • 19 datasets
Molecular property prediction is the task of predicting the properties of a molecule from its structure.
Benchmarks
These leaderboards are used to track progress in Molecular Property Prediction
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Libraries
Use these libraries to find Molecular Property Prediction models and implementationsDatasets
MoleculeNet
QM9
Tox21
QM7
SIDER
Subtasks
Latest papers with no code
Molecular Property Prediction Based on Graph Structure Learning
Following that, we conduct graph structure learning on the MSG (i. e., molecule-level graph structure learning) to get the final molecular embeddings, which are the results of fusing both GNN encoded molecular representations and the relationships among molecules, i. e., combining both intra-molecule and inter-molecule information.
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy
We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks.
Pre-training of Molecular GNNs via Conditional Boltzmann Generator
We show that our model has a better prediction performance for molecular properties than existing pre-training methods using molecular graphs and three-dimensional molecular structures.
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction
We explore the underlying topologies and patterns in molecular structures by applying Vietoris-Rips persistent homology across varying scales and parameters such as atomic weight, partial charge, bond type, and chirality.
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
For the first time, we propose a sound notion of adversarial robustness that accounts for task equivariance.
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
An effective representation of drug molecules emerges as a pivotal component in this pursuit.
Multiparameter Persistent Homology for Molecular Property Prediction
In this study, we present a novel molecular fingerprint generation method based on multiparameter persistent homology.
From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning
Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks.
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds
Thus, we introduce a similarity search and clustering algorithm to aid in searching for and interacting with molecules, enhancing efficiency in chemical exploration and enabling future development of emergent properties in molecular property prediction models.
