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/yerevann/chemlactica
https://github.com/ying531/conformal-selection
https://github.com/ykrmm/sota_gnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yujiali/ggnn
https://github.com/yvquanli/GLAM
https://github.com/yvquanli/TrimNet
https://github.com/yzhao062/yzhao062
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
https://github.com/zhiqiang00/Hon-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/zhiqiangzhongddu/la3
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
https://github.com/ziqi92/Modof
A Deep Generative Model for Molecule Optimization via One Fragment Modification
https://gitlab.com/bpaassen/ast2vec
https://gitlab.com/inpefess/ast2vec
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.