Papers with Code 아카이브

Drug Discovery

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 3
평가표: 28
보존된 평가 기록: 96

Drug discovery is the task of applying machine learning to discover new candidate drugs.

<span style="color:grey; opacity: 0.6">( Image credit: A Turing Test for Molecular Generators )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/gnn4dr/DRKG

Strategies for Pre-training Graph Neural Networks

https://github.com/gregory-kyro/chemspaceal

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

https://github.com/guanjq/targetdiff

Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models

https://github.com/hazdzz/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/hits-mbm-dev/grappa

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/hld67890/prml2020_pj

Strategies for Pre-training Graph Neural Networks

https://github.com/hoffmannjordan/Encoding-Decoding-3D-Crystals

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

https://github.com/insilicomedicine/fcd_torch

Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery

https://github.com/intellabs/matsciml

Directional Message Passing for Molecular Graphs

https://github.com/ixsluo/cond-cdvae

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

https://github.com/jacquesboitreaud/DeepFRED

Strategies for Pre-training Graph Neural Networks

https://github.com/jacquesboitreaud/rna_ne

Strategies for Pre-training Graph Neural Networks

https://github.com/jaechanglim/GFML

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/jaechanglim/GGM

Scaffold-based molecular design using graph generative model

https://github.com/jaechanglim/molecule-generator

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

https://github.com/jamesdolezal/slideflow

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

https://github.com/jbr-ai-labs/lipophilicity-prediction

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/jiangboahu/glcn-tf

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/jonnor/datascience-master

Self-Normalizing Neural Networks

https://github.com/junxia97/progcl

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

https://github.com/junxia97/simgrace

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

https://github.com/kaize0409/GPN_Graph-Few-shot

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

https://github.com/karanY2019/Visualizing-Deep-Graph-Generative-Models-for-Drug-Discovery

Visualizing Deep Graph Generative Models for Drug Discovery

https://github.com/khanh94/jtnnpython3

Junction Tree Variational Autoencoder for Molecular Graph Generation

https://github.com/kiharalab/gnn_pocket

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/kimisyo/simple-GCN

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/knc6/cdvae

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

https://github.com/knowlab/bi-weekly-paper-presentation

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

https://github.com/kntkb/openmmforcefields

End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/leffff/any-domain-pretrain-gnns

Strategies for Pre-training Graph Neural Networks

https://github.com/lehgtrung/egfr-att

Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors

https://github.com/lfhase/gala

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

https://github.com/lifanchen-simm/transformerCPI

https://github.com/lipingcoding/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/lucidrains/molecule-attention-transformer

Molecule Attention Transformer

https://github.com/lukecavabarrett/pna

Neural Message Passing for Quantum Chemistry

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.