Molecular Property Prediction
121 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
Efficient Sharpness-aware Minimization for Molecular Graph Transformer Models
YL-wang/GraphSAM
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ICLR 2024
Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and mitigate generalization degradation.
Generalizable, Fast, and Accurate DeepQSPR with fastprop Part 1: Framework and Benchmarks
Quantitative Structure Property Relationship studies aim to define a mapping between molecular structure and arbitrary quantities of interest.
A Python library for efficient computation of molecular fingerprints
In this project, we created a Python library that computes molecular fingerprints efficiently and delivers an interface that is comprehensive and enables the user to easily incorporate the library into their existing machine learning workflow.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
shamim-hussain/egt_pytorch
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We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
dmis-lab/molpla
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Molecular core structures and R-groups are essential concepts in drug development.
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks.
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction
TwinBooster enables the prediction of properties of unseen bioassays and molecules by providing state-of-the-art zero-shot learning tasks.
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process.
Enhancing Molecular Property Prediction via Mixture of Collaborative Experts
Hyacinth-YX/mixture-of-collaborative-experts
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To address data scarcity and imbalance in MPP, some studies have adopted Graph Neural Networks (GNN) as an encoder to extract commonalities from molecular graphs.
Removing Biases from Molecular Representations via Information Maximization
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug.
