Property Prediction
236 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in Property Prediction
Libraries
Use these libraries to find Property Prediction models and implementationsMost implemented papers
Self-Supervised Graph Transformer on Large-Scale Molecular Data
tencent-ailab/grover
•
•
NeurIPS 2020
We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
seyonechithrananda/bert-loves-chemistry
•
•
GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.
Path-Augmented Graph Transformer Network
benatorc/PA-Graph-Transformer
•
•
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
Optimal Transport Graph Neural Networks
benatorc/OTGNN
•
•
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133, 000 species from QM9, and 317, 000 species with experimental data related to biophysics, physiology, and physical chemistry.
