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

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Use these libraries to find Molecular Docking models and implementations
2 papers
1,222

Most implemented papers

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

deepchem/deepchem 30 Mar 2017

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

lucagl/moad_ligandfinder 13 Jun 2022

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 4 Oct 2022

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

dptech-corp/Uni-Mol 20 May 2024

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

bimsbbioinfo/compassdock 10 Jun 2024

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

tmlr-group/fabflex 20 Feb 2025

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

nguyentrunghai/BPMFwFFT 27 Jul 2017

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

YanjunLi-CS/DeepAtom_SupplementaryMaterials 1 Dec 2019

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

aspuru-guzik-group/assessing_mol_prediction_confidence 23 Feb 2021

Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.