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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/weilabmsu/svs
공식 코드SVSBI: Sequence-based virtual screening of biomolecular interactions
https://github.com/youqingxiaozhua/deepternary
공식 코드SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation
https://github.com/zjunlp/MolGen
공식 코드https://github.com/zrqiao/NeuralPLexer
공식 코드State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
https://github.com/GilbertoQ/Bioinformatics
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
https://github.com/bioinfomachinelearning/posebench
FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
https://github.com/chenruduan/oareactdiff
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
https://github.com/deepchem/deepchem
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
https://github.com/deepchem/moleculenet
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
https://github.com/deepmodeling/Uni-Mol
Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
https://github.com/nguyentrunghai/BPMFwFFT
Using the Fast Fourier Transform in Binding Free Energy Calculations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.