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Drug Discovery

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전체 기법 전체 데이터셋

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/arnaudporet/kali

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Therapeutic target discovery using Boolean network attractors: improvements of kali

https://github.com/arthurcerveira/mtdd-evaluation-framework

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Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study

https://github.com/articlesmli/CID_SID_ML_model

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Predicting Novel Functional Roles of Designed Small Biomolecules: An ML Approach Utilizing PubChem Compound and Substance Identifiers (CID-SID ML model)

https://github.com/ask-berkeley/coarsenconf

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CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

https://github.com/aspuru-guzik-group/quantum-generative-models

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Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS

https://github.com/astrazeneca/awesome-drug-discovery-knowledge-graphs

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A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

https://github.com/astrazeneca/biomedical-kg-topological-imbalance

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Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

https://github.com/astrazeneca/kazu

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Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework

https://github.com/astrazeneca/kgem-in-drug-discovery

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Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

https://github.com/astrazeneca/selfpad

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Improving Antibody Humanness Prediction using Patent Data

https://github.com/atomwiseinc/cslvae

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An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

https://github.com/azureleon1/awesome-molecular-diffusion-models

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Diffusion Models for Molecules: A Survey of Methods and Tasks

https://github.com/barabasi-lab/clinical_trials

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The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery

https://github.com/batistagroup/chemspaceal

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ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

https://github.com/bbrighttaer/irelease

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Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules

https://github.com/bbrighttaer/ivpgan

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Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints

https://github.com/bbrighttaer/jova_baselines

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Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

https://github.com/bdjones13/flagser-laplacian

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Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction

https://github.com/beanmah/egsteal

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How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment

https://github.com/benatorc/OTGNN

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Optimal Transport Graph Neural Networks

https://github.com/benevolentAI/guacamol

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GuacaMol: Benchmarking Models for De Novo Molecular Design

https://github.com/benevolentAI/guacamol_baselines

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GuacaMol: Benchmarking Models for De Novo Molecular Design

https://github.com/bimsbbioinfo/pocketvina

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PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning

https://github.com/binghong-ml/retro_star

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RetroGraph: Retrosynthetic Planning with Graph Search

https://github.com/biofm/openbiomed

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MolFM: A Multimodal Molecular Foundation Model

https://github.com/biogemt/agentomics-ml

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Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data

https://github.com/bioinf-jku/FCD

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Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery

https://github.com/bioinf-jku/SNNs

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Self-Normalizing Neural Networks

https://github.com/bioinf-jku/interpretable_ml_drug_discovery

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Interpretable Deep Learning in Drug Discovery

https://github.com/bioinfomachinelearning/deepinteract

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Geometric Transformers for Protein Interface Contact Prediction

https://github.com/bioinfomachinelearning/flowdock

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FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction

https://github.com/bioinfomachinelearning/posebench

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Deep Learning for Protein-Ligand Docking: Are We There Yet?

https://github.com/bioinfomachinelearning/psbench

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PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models

https://github.com/biomed-AI/MolRep

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Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

https://github.com/biomedsciai/biomed-multi-alignment

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MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language

https://github.com/blazerye/drugassist

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DrugAssist: A Large Language Model for Molecule Optimization

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