Molecular Docking
34 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
These leaderboards are used to track progress in Molecular Docking
Libraries
Use these libraries to find Molecular Docking models and implementations
Most implemented papers
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose.
SHREC 2022: Protein-ligand binding site recognition
This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
gcorso/diffdock
•
•
We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs.
CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning
Our results show that, while fine-tuning without Compass improves the percentage of docked poses with RMSD < 2{\AA}, it leads to a decrease in physical/chemical and bioactivity favorability.
Fast 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.
Using the Fast Fourier Transform in Binding Free Energy Calculations
According to implicit ligand theory, the standard binding free energy is an exponential average of the binding potential of mean force (BPMF), an exponential average of the interaction energy between the ligand apo ensemble and a rigid receptor.
DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction
The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts, especially a chemical compound, i. e., a ligand, and a protein.
DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations
cansyl/DEEPScreen
•
•
Chemical Science 2020
The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery.
Assigning Confidence to Molecular Property Prediction
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.
