https://github.com/seojin-kim/hi-mol
공식 코드Data-Efficient Molecular Generation with Hierarchical Textual Inversion
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/seonghwanseo/molvoxel
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/seonghwanseo/pharmaconet
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/seullee05/geam
공식 코드https://github.com/seullee05/mood
공식 코드Exploring Chemical Space with Score-based Out-of-distribution Generation
https://github.com/shaharharel/CDN_Molecule
공식 코드Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
https://github.com/shamim-hussain/tgt
공식 코드https://github.com/sharc-lab/flowgnn
공식 코드FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
https://github.com/sharc-lab/gengnn
공식 코드GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
https://github.com/shennong-program/mlmd
공식 코드https://github.com/shennong-program/pycgs
공식 코드https://github.com/shennong-program/shennongname
공식 코드https://github.com/shenoynikhil/etflow
공식 코드ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
https://github.com/shmily-ld/dynamicdta
공식 코드https://github.com/shuix007/eip4nnpotentials
공식 코드https://github.com/sjiang87/deephyper
공식 코드Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
https://github.com/smortezah/napr
공식 코드The chemical space of terpenes: insights from data science and AI
https://github.com/smu-tao-group/ADMET_XGBoost
공식 코드https://github.com/smu-tao-group/passerrank
공식 코드PASSerRank: Prediction of Allosteric Sites with Learning to Rank
https://github.com/snap-stanford/pretrain-gnns/
공식 코드https://github.com/snudatalab/AugWard
공식 코드AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
https://github.com/songtaoliu0823/crebm
공식 코드Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
https://github.com/songtaoliu0823/fusionretro
공식 코드FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
https://github.com/ssggreg/comrecgc
공식 코드COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
https://github.com/sshaghayeghs/llama-vs-chatgpt
공식 코드https://github.com/sshaghayeghs/llama-vs-gpt
공식 코드https://github.com/stanlazic/ML_uncertainty_quantification
공식 코드Quantifying sources of uncertainty in drug discovery predictions with probabilistic models
https://github.com/statbiophys/abgen
공식 코드https://github.com/sxkdz/marcel
공식 코드Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
https://github.com/tansey/functionalmf
공식 코드https://github.com/taoyuw/lagdif
공식 코드LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble
https://github.com/tda-jyamiti/d-gril
공식 코드D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
https://github.com/teganmaharaj/zoneout
공식 코드https://github.com/tencent-ailab/DrugOOD
공식 코드https://github.com/theislab/chemCPA
공식 코드Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
https://github.com/thematrixmaster/omics-guided-gfn
공식 코드Cell Morphology-Guided Small Molecule Generation with GFlowNets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.