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Drug Discovery

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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/blender-nlp/MolT5

공식 코드

Translation between Molecules and Natural Language

https://github.com/boun-tabi/chemboost

공식 코드

ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction

https://github.com/boun-tabi/debiaseddta-reproduce

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DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models

https://github.com/boun-tabi/pydta

공식 코드

DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models

https://github.com/bowen-gao/sbdd_practical_evaluation

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From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics

https://github.com/brain-research/mpnn

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Neural Message Passing for Quantum Chemistry

https://github.com/bupt-gamma/mosedti

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Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction

https://github.com/bytedance/byteff

공식 코드

Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage

https://github.com/bytedance/decompdiff

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DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

https://github.com/calvin-zcx/moflow

공식 코드

MoFlow: An Invertible Flow Model for Generating Molecular Graphs

https://github.com/canisw/tf3p

공식 코드

TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network

https://github.com/carpenter-singh-lab/motive

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MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction

https://github.com/causalbench/causalbench-starter

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CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

https://github.com/cdal-soc/predict-ai

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Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information

https://github.com/cfredinh/coda

공식 코드

Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation

https://github.com/chandlerbang/decorr

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Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective

https://github.com/chandlerbang/gtrans

공식 코드

Empowering Graph Representation Learning with Test-Time Graph Transformation

https://github.com/chao1224/NeuralMD

공식 코드

A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics

https://github.com/chao1224/chatdrug

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ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback

https://github.com/chao1224/moleculesde

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A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

https://github.com/chao1224/moleculestm

공식 코드

Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing

https://github.com/chao1224/sgnn-ebm

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Structured Multi-task Learning for Molecular Property Prediction

https://github.com/chatterjeeayan/ai-bind

공식 코드

AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands

https://github.com/chilab1/hge

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Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability

https://github.com/choderalab/espaloma

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End-to-End Differentiable Molecular Mechanics Force Field Construction

https://github.com/choderalab/espaloma-0.3.0-manuscript

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/choderalab/geometry-benchmark-espaloma

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/choderalab/pl-benchmark-espaloma-experiment

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/choderalab/refit-espaloma

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/choderalab/vanilla-espaloma-experiment

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

https://github.com/cieplinski-tobiasz/smina-docking-benchmark

공식 코드

We Should at Least Be Able to Design Molecules That Dock Well

https://github.com/clickff/adseqgan

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Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation

https://github.com/coleygroup/del_qsar

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Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function

https://github.com/coleygroup/molpal

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Accelerating high-throughput virtual screening through molecular pool-based active learning

https://github.com/crosszamirski/guided-i2i

공식 코드

Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels

https://github.com/csinva/imodels

공식 코드

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.