Papers with Code 아카이브
Drug Discovery
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/shubhamgupta5893/GraphRNN
MolecularRNN: Generating realistic molecular graphs with optimized properties
https://github.com/shubhamguptaiitd/GraphRNN
MolecularRNN: Generating realistic molecular graphs with optimized properties
https://github.com/smiles724/protmd
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
https://github.com/snap-stanford/pretrain-gnns
https://github.com/steshinss/lohi_neurips2023
https://github.com/steshinss/lohi_splitter
https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/susanzhang233/mollykill_2.0
https://github.com/swansonk14/chemprop
Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/teddykoker/mpnn-for-quantum-chem
https://github.com/temibabs/I-RGCN
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/thinng/GraphDTA
https://github.com/thumnlab/autogl-light
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
https://github.com/tiger-tiger/MOSES-2020
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/tisabe/jraph_mpeu
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/tk-rusch/graphcon
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/keras-gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tmatcham/CausalBART
https://github.com/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tomdbar/eco-dqn
https://github.com/toshi-k/kaggle-champs-scalar-coupling
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/toshi-k/kaggle-trends-assessment-prediction
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/txie-93/cdvae
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/uhlerlab/covid19_repurposing
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
https://github.com/vittorioorlandi/drbart
https://github.com/vntchain/gnnscvuldetector
https://github.com/wengong-jin/hgraph2graph
https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv
https://github.com/yCobanoglu/infinite-width-gnns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangjun1994/CAGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yanhuchen/quantum-graph-convolutional-network
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.