Molecular Property Prediction

128 papers with code • 18 benchmarks • 19 datasets

Molecular property prediction is the task of predicting the properties of a molecule from its structure.


Use these libraries to find Molecular Property Prediction models and implementations

Most implemented papers

Neural Message Passing for Quantum Chemistry

brain-research/mpnn ICML 2017

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

Strategies for Pre-training Graph Neural Networks

snap-stanford/pretrain-gnns ICLR 2020

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.

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

fanyun-sun/InfoGraph ICLR 2020

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.

Analyzing Learned Molecular Representations for Property Prediction

swansonk14/chemprop 2 Apr 2019

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.

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

bingsu12/momu 12 Sep 2022

Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.

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 19 Oct 2020

GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs

divelab/MoleculeX 30 Sep 2021

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

ibm/graph_space_gps 3 Nov 2022

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

shamim-hussain/tgt 7 Feb 2024

We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.