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 with no code
Transformers for molecular property prediction: Lessons learned from the past five years
Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science.
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints
Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction.
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models
Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs).
Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications.
The Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey
The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization.
ChemLLM: A Chemical Large Language Model
ChemLLM beats GPT-3. 5 on all three principal tasks in chemistry, i. e., name conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on two of them.
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Contrary to prior work, we propose a novel 2D--3D aggregation mechanism based on a differentiable solver for the \emph{Fused Gromov-Wasserstein Barycenter} problem and the use of an efficient online conformer generation method based on distance geometry.
Graph Multi-Similarity Learning for Molecular Property Prediction
Additionally, previous multi-similarity approaches require the specification of positive and negative pairs to attribute distinct predefined weights to different relative similarities, which can introduce potential bias.
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction
The advantage of the multimodal model lies in its ability to process diverse sources of data using proper models and suitable fusion methods, which would enhance the noise resistance of the model while obtaining data diversity.
Molecular Property Prediction Based on Graph Structure Learning
Following that, we conduct graph structure learning on the MSG (i. e., molecule-level graph structure learning) to get the final molecular embeddings, which are the results of fusing both GNN encoded molecular representations and the relationships among molecules, i. e., combining both intra-molecule and inter-molecule information.
