molecular representation
59 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in molecular representation
Libraries
Use these libraries to find molecular representation models and implementationsMost implemented papers
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.
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
•
•
GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations
In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures.
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
aspuru-guzik-group/selfies
•
•
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
Molecular representation learning with language models and domain-relevant auxiliary tasks
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems.
Multiresolution Equivariant Graph Variational Autoencoder
In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner.
SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction
kanz76/ssi-ddi
•
•
Briefings in Bioinformatics 2021
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug–drug interactions (DDIs), which can cause serious injuries to the organism.
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
chao1224/se3ddm
•
•
Further by leveraging an SE(3)-invariant score matching method, we propose GeoSSL-DDM in which the coordinate denoising proxy task is effectively boiled down to denoising the pairwise atomic distances in a molecule.
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
jiaor17/3D-MGP
•
•
Pretraining molecular representation models without labels is fundamental to various applications.
