Papers Blind Docking
“Blind Docking” 태그가 달린 논문 19편 · 필터 해제
Seq2Bind Webserver for Decoding Binding Hotspots directly from Sequences using Fine-Tuned Protein Language Models
Decoding protein-protein interactions (PPIs) at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind Webserver, a computational framework that lever…
Blind DockingPoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks requiring extensive training, posing cha…
Blind DockingMolecular DockingSE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation
Targeted protein degradation (TPD) induced by small molecules has emerged as a rapidly evolving modality in drug discovery, targeting proteins traditionally considered "undruggable". Proteolysis-targeting chimeras (PROTA…
Blind DockingDecoderDrug DiscoveryGraph Attention+1Fast and Accurate Blind Flexible Docking
Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets, plays a vital role in drug discovery. However, existing docking methods often face limitations: they either over…
Blind DockingComputational EfficiencyDrug DiscoveryMolecular Docking+1Accurate Pocket Identification for Binding-Site-Agnostic Docking
Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To addr…
Blind DockingDrug DesignGroup Ligands Docking to Protein Pockets
Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand …
Blind DockingMolecular DockingFlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly mo…
Blind DockingDrug DiscoveryGNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins. Despite the availability of numerous d…
Blind DockingDrug DiscoveryGraph Neural NetworkMolecular DockingDeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leve…
Blind DockingDrug DesignMolecular DockingPrediction+1GeoDirDock: Guiding Docking Along Geodesic Paths
This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along…
Blind DockingDenoisingDrug DiscoveryMolecular DockingFABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are often slow and costly. The advent of dee…
Blind DockingDrug DiscoveryMolecular DockingDeep Confident Steps to New Pockets: Strategies for Docking Generalization
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigor…
Blind DockingQuantum-Inspired Machine Learning for Molecular Docking
Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sa…
Blind DockingCombinatorial OptimizationDeep LearningDrug Design+1Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets, which is fundamental in the design of novel drugs. Despite recent advancements in geometr…
Blind DockingGPUMolecular DockingFABind: Fast and Accurate Protein-Ligand Binding
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in ad…
Blind DockingDrug DiscoveryPose EstimationregressionDSDP: A Blind Docking Strategy Accelerated by GPUs
Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking task. The traditional docking methods are…
Blind DockingDrug DiscoveryMolecular DockingDiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have de…
Blind DockingDrug DesignMolecular DockingState-specific protein-ligand complex structure prediction with a multi-scale deep generative model
The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematicall…
BenchmarkingBlind DockingProtein FoldingProtein Structure Prediction+1EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or d…
Blind DockingDeep LearningDrug Discovery