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/TrustAI/DeepConcolic
https://github.com/UlionTse/mlgb
https://github.com/VEK239/StructGNN-lipophilicity
https://github.com/VirginiaSabando/MolecularEmbeddings
Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?
https://github.com/Yindong-Zhang/GraphConvolutionDrugTargetInteration
https://github.com/ZJULearning/graph_level_drug_discovery
https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/abi-kothapalli/minimumdominatingsets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/aclyde11/RNNGenerator
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
https://github.com/ajbisberg/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/akirasosa/pytorch-dimenet
https://github.com/alexOarga/haiku-geometric
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gated_graph_conv.py
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ardigen/MAT
https://github.com/aricsvenz/AI_for_Drugs
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
https://github.com/asif-salim/GCN-Graph_Convolutional_Network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ask-berkeley/badger-sbdd
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
https://github.com/aszot/ggnn
https://github.com/austint/mol_ga
Genetic algorithms are strong baselines for molecule generation
https://github.com/basiralab/RG-Select
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/Reproducible-Generative-Learning
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/reproduciblefedgnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bcsrn/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bdqnghi/bi-tbcnn
https://github.com/bepping/non-oversmoothing-gcns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/biolearning-stadius/rdkit
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
https://github.com/blackPacha/VAE_ABIDE1
https://github.com/bowen-gao/drugclip
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
https://github.com/cbilodeau2/g2g_optimization
Hierarchical Generation of Molecular Graphs using Structural Motifs
https://github.com/chemprop/chemprop
Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/chingyaoc/ggnn.pytorch
https://github.com/clin366/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/code-implementation1/Code9/tree/main/pafnucy
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.