https://github.com/zaixizhang/molcode
공식 코드An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Papers with Code 아카이브
Drug Discovery
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 3
- 평가표
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- 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/zehong-wang/gft
공식 코드GFT: Graph Foundation Model with Transferable Tree Vocabulary
https://github.com/zehong-wang/tans
공식 코드https://github.com/zengjingqi/graphai-for-tcm
공식 코드Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine
https://github.com/zetayue/MXMNet
공식 코드Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
https://github.com/zhangruochi/pepharmony
공식 코드https://github.com/zhangtia16/molgraph-lardo
공식 코드https://github.com/zhaohanm/fusiondti
공식 코드FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
https://github.com/zhichunguo/Meta-MGNN
공식 코드https://github.com/zhiqiangzhongddu/awesome-knowledge-augmented-gml-for-drug-discovery
공식 코드Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
https://github.com/zhousongh/hipm
공식 코드https://github.com/zishanshu/mtpnet
공식 코드MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
https://github.com/zoisboukouvalas/pyiva
공식 코드https://gitlab.com/bpaassen/rtgae
공식 코드https://gitlab.com/cheminfIBB/kalasanty
공식 코드Improving detection of protein-ligand binding sites with 3D segmentation
https://gitlab.com/cheminfIBB/pafnucy
공식 코드Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
https://gitlab.com/s3anaz/multi-fidelity-cost-aware-bayesian-optimization
공식 코드https://huggingface.co/alimotahharynia/DrugGen
공식 코드DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback
https://huggingface.co/spaces/alimotahharynia/GPT-2-Drug-Generator
공식 코드DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback
https://huggingface.co/spaces/mschuh/BarlowDTI
공식 코드Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction
https://huggingface.co/zequnl/molxpt
공식 코드MolXPT: Wrapping Molecules with Text for Generative Pre-training
https://github.com/0h-n0/cdn_molecule_pytorch
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/pafnucy
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
https://github.com/2023-MindSpore-4/Code11/tree/main/snn_mlp
https://github.com/2023-MindSpore-4/Code7/tree/main/snn_mlp
https://github.com/2024-MindSpore-1/Code3/tree/main/Dimenet
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
https://github.com/2024-MindSpore-1/Code3/tree/main/TransformerCPI
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/595693085/DGraphDTA
https://github.com/ADL-TBB/tbb-bridge-dpi
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
https://github.com/AJS1NGH/Predicting-Molecular-Properties-Challenge
https://github.com/ATOMScience-org/AMPL
https://github.com/BenjiKCF/SNN-with-embeddings-for-Malware-Prediction
https://github.com/Bibyutatsu/FastJTNNpy3
Junction Tree Variational Autoencoder for Molecular Graph Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.