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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/UCL-CCS/ML-PB-Covid-drug

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Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

https://github.com/Wenlin-Chen/ADKF-IFT

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Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN

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Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction

https://github.com/YanjunLi-CS/DeepAtom_SupplementaryMaterials

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DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

https://github.com/Yinghao-Li/UncertaintyBenchmark

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/Yonggie/ResMGCN

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ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering

https://github.com/ZeXingZ/DIG-Mol

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Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

https://github.com/Zhaoxiang0422/SOC-DGL

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SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification

https://github.com/Zhaoyang-Chu/HGRL-DTA

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Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

https://github.com/Zhenger959/MoleculeCLA

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MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis

https://github.com/aaronli43/grappi

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GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration

https://github.com/abhinadduri/panspecies-dti

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Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT

https://github.com/ace-kaist/pignet2

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PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening

https://github.com/acellera/acegen-open

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ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

https://github.com/acellera/quantumbind_rbfe

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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials

https://github.com/acharkq/next-mol

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NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation

https://github.com/adpartin/cross-dataset-drp-paper

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Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis

https://github.com/adpartin/dr-learning-curves

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Learning Curves for Drug Response Prediction in Cancer Cell Lines

https://github.com/agave233/SIGN

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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

https://github.com/ahaubioinformatics/cetexplainer

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Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction

https://github.com/ahu-bioinf-lab/amgdti

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Heterogeneous networks in drug-target interaction prediction

https://github.com/ai-hpc-research-team/git-mol

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GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text

https://github.com/ai-hpc-research-team/slm4crp

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A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions

https://github.com/ai-in-complex-systems-lab/hsc-grn-gnn

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Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks

https://github.com/ai-med/almgig

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Adversarial Learned Molecular Graph Inference and Generation

https://github.com/aidotse/leakpro

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Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy

https://github.com/aimat-lab/megan_aggregators

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Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI

https://github.com/airi-institute/golf

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Gradual Optimization Learning for Conformational Energy Minimization

https://github.com/ajfisch/conformal-cascades

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Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

https://github.com/ajfisch/few-shot-cp

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Few-shot Conformal Prediction with Auxiliary Tasks

https://github.com/alimotahharynia/GPT-2-Drug-Generator

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DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback

https://github.com/amerorchis/Hybrid-Quantum-GAN

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Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation

https://github.com/amitaysicherman/reactembed

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ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction Networks

https://github.com/amshoreline/sae

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Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation

https://github.com/aqlaboratory/quickbind

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QuickBind: A Light-Weight And Interpretable Molecular Docking Model

https://github.com/arch4ngel21/scikit-fingerprints

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A Python library for efficient computation of molecular fingerprints

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