# Molecular Property Prediction

83 papers with code • 18 benchmarks • 8 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 implementations## Most implemented papers

# Neural Message Passing for Quantum Chemistry

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

# Strategies for Pre-training Graph Neural Networks

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

# Analyzing Learned Molecular Representations for Property Prediction

In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.

# InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.

# Self-Supervised Graph Transformer on Large-Scale Molecular Data

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.

# 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.

# 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.