Papers with Code 아카이브
Drug Discovery
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/jlparki/xgpr
공식 코드https://github.com/jonasjuerss/help
공식 코드Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
https://github.com/jostorge/diffusion-hopping
공식 코드DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
https://github.com/jundeli/quantum-gan
공식 코드https://github.com/junxia97/awesome-pre-training-on-graphs
공식 코드A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
https://github.com/justinwjl/gtb-dti
공식 코드Benchmark on Drug Target Interaction Modeling from a Structure Perspective
https://github.com/jxzhangjhu/MatDesINNe
공식 코드Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
https://github.com/kaist-silab/symmetric_replay
공식 코드https://github.com/kaize0409/GPN
공식 코드Graph Prototypical Networks for Few-shot Learning on Attributed Networks
https://github.com/karannb/interact
공식 코드Implementation and Application of an Intelligibility Protocol for Interaction with an LLM
https://github.com/ketatam/diffdock-pp
공식 코드DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
https://github.com/kexinhuang12345/DeepPurpose
공식 코드DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
https://github.com/kexinhuang12345/MolTrans
공식 코드MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
https://github.com/khodabandeh-ali/bokdiff
공식 코드BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation
https://github.com/kimyong95/guide-stable-diffusion
공식 코드Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
https://github.com/kirkegaardlab/splender
공식 코드https://github.com/kiyoung98/tps-dps
공식 코드Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
https://github.com/klemens-floege/oneprot
공식 코드https://github.com/klicperajo/dimenet
공식 코드https://github.com/kramerlab/multi-omics_analysis
공식 코드https://github.com/ku-dml/mol-infer
공식 코드A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility
https://github.com/kvrsid/toxic
공식 코드Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data
https://github.com/kxz18/UniMoMo
공식 코드UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
https://github.com/kxz18/gp-vae
공식 코드Molecule Generation by Principal Subgraph Mining and Assembling
https://github.com/kyrs/wiser
공식 코드https://github.com/lanagarmire/asgard
공식 코드ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs
https://github.com/lars-research/3d-pgt
공식 코드https://github.com/lasgroup/lite
공식 코드LITE: Efficiently Estimating Gaussian Probability of Maximality
https://github.com/laurendelong21/mars
공식 코드MARS: A neurosymbolic approach for interpretable drug discovery
https://github.com/laurendelong21/moa-net
공식 코드MARS: A neurosymbolic approach for interpretable drug discovery
https://github.com/learningmatter-mit/NeuralForceField
공식 코드https://github.com/lee-jwon/bind
공식 코드https://github.com/leojklarner/context-guided-diffusion
공식 코드Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
https://github.com/leojklarner/q-savi
공식 코드Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
https://github.com/lfhase/ciga
공식 코드Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.