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.
Libraries
Use these libraries to find Molecular Property Prediction models and implementationsDatasets
Subtasks
Most implemented papers
Path-Augmented Graph Transformer Network
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
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
ChemBERTa-2: Towards Chemical Foundation Models
Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks.
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
Lo-Hi: Practical ML Drug Discovery Benchmark
We analyzed modern benchmarks and showed that they are unrealistic and overoptimistic.
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
This paper introduces the N-gram graph, a simple unsupervised representation for molecules.
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction.