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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
Seq2Bind Webserver for Decoding Binding Hotspots directly from Sequences using Fine-Tuned Protein Language Models
ReassembleNet: Learnable Keypoints and Diffusion for 2D Fresco Reconstruction
Prediction of Novel CXCR7 Inhibitors Using QSAR Modeling and Validation via Molecular Docking
Multi-Ligand Simultaneous Docking Analysis of Moringa Oleifera Phytochemicals Reveals Enhanced BCL-2 Inhibition via Synergistic Action
Computational Analysis using Multi-ligand Simultaneous Docking of Withaferin A and Garcinol Reveals Enhanced BCL-2 and AKT-1 Inhibition
In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches
Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning
PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation
Fast and Accurate Blind Flexible Docking
Accurate Pocket Identification for Binding-Site-Agnostic Docking
Generative Modeling on Lie Groups via Euclidean Generalized Score Matching
Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration
Group Ligands Docking to Protein Pockets
AI Model for Predicting Binding Affinity of Antidiabetic Compounds Targeting PPAR
PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction
FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
In Silico Pharmacokinetic and Molecular Docking Studies of Natural Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer
Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening
QuickBind: A Light-Weight And Interpretable Molecular Docking Model
RapidDock: Unlocking Proteome-scale Molecular Docking
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
To Explore the Potential Inhibitors against Multitarget Proteins of COVID 19 using In Silico Study
Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets
Understanding active learning of molecular docking and its applications
Scoreformer: A Surrogate Model For Large-Scale Prediction of Docking Scores
CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning
Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
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