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Molecular Docking

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Predicting the binding structure of a small molecule ligand to a protein, which is critical to drug design.

Description from: DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/BC3D/BC3D_2021

공식 코드

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

https://github.com/HBioquant/DiffBindFR

공식 코드

DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking

https://github.com/HannesStark/EquiBind

공식 코드

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

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

공식 코드

DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

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

공식 코드

Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT

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

공식 코드

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction

https://github.com/aqlaboratory/quickbind

공식 코드

QuickBind: A Light-Weight And Interpretable Molecular Docking Model

https://github.com/aspuru-guzik-group/assessing_mol_prediction_confidence

공식 코드

Assigning Confidence to Molecular Property Prediction

https://github.com/balytskyijaroslaw/rapid-net

공식 코드

Accurate Pocket Identification for Binding-Site-Agnostic Docking

https://github.com/bimsbbioinfo/compass

공식 코드

CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning

https://github.com/bimsbbioinfo/compassdock

공식 코드

CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning

https://github.com/bimsbbioinfo/pocketvina

공식 코드

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

https://github.com/bioinfomachinelearning/flowdock

공식 코드

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

https://github.com/bjing2016/scalar-fields

공식 코드

Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms

https://github.com/cataai/posex

공식 코드

PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking

https://github.com/cfifty/ignite

공식 코드

Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction

https://github.com/concept-lab/shrec22_proteinligandbenchmark

공식 코드

SHREC 2022: Protein-ligand binding site recognition

https://github.com/directmolecularconfgen/dmcg

공식 코드

Direct Molecular Conformation Generation

https://github.com/dockstring/dockstring

공식 코드

DOCKSTRING: easy molecular docking yields better benchmarks for ligand design

https://github.com/dptech-corp/Uni-Mol

공식 코드

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

https://github.com/drorlab/glow_ives

공식 코드

Enhancing Ligand Pose Sampling for Molecular Docking

https://github.com/fairydance/deeprli

공식 코드

DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction

https://github.com/gcorso/diffdock

공식 코드

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

https://github.com/gcorso/disco-diffdock

공식 코드

DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents

https://github.com/insitro/insitro-research

공식 코드

DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries

https://github.com/jasonkim8652/al_breakdown

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Understanding active learning of molecular docking and its applications

https://github.com/jiaxianyan/DeltaDock

공식 코드

DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking

https://github.com/ldeng0205/confidence-bootstrapping

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Deep Confident Steps to New Pockets: Strategies for Docking Generalization

https://github.com/lucagl/moad_ligandfinder

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SHREC 2022: Protein-ligand binding site recognition

https://github.com/molecularmodelinglab/plantain

공식 코드

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking

https://github.com/parham-rezaee/bindingdb-bc-virtual-screening

공식 코드

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-kB receptors based on machine learning and molecular docking

https://github.com/polowitty/covdocker

공식 코드

CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions

https://github.com/qizhipei/fabind

공식 코드

FABind: Fast and Accurate Protein-Ligand Binding

https://github.com/rivas-lab/Smiles2Dock

공식 코드

Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking

https://github.com/rose-stl-lab/mf-lal

공식 코드

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning

https://github.com/stemylonas/deepsurf_shrec22

공식 코드

SHREC 2022: Protein-ligand binding site recognition

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