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CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

https://doi.org/10.1021/ci200082t

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Ligand−Protein DataBase: Linking Protein−Ligand Complex Structures to Binding Data

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Recent improvements to Binding MOAD: a resource for protein–ligand binding affinities and structures

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PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers

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Parallel Computing in Deep Learning: Bioinformatics Case Studiesa

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Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction

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MHAN-DTA: A Multiscale Hybrid Attention Network for Drug-Target Affinity Prediction

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NciaNet: A Non-Covalent Interaction-Aware Graph Neural Network for the Prediction of Protein-Ligand Interaction in Drug Discovery

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Geometric Deep Learning for Protein-Ligand Affinity Prediction With Hybrid Message Passing Strategies

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A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction

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Equivariant Interaction-Aware Graph Network for Predicting the Binding Affinity of Protein-Ligand

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Deep Learning-Driven Protein-Ligand Binding Affinity Prediction: Data, Architecture, Training and Evaluation

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Hierarchical Graph Representation Learning From a Statistical Perspective for Generalizable and Interpretable Protein-Ligand Binding Affinity Prediction

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GIaNt: Protein-Ligand Binding Affinity Prediction via Geometry-Aware Interactive Graph Neural Network

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Local–Global Structure-Aware Geometric Equivariant Graph Representation Learning for Predicting Protein–Ligand Binding Affinity

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Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures

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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

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Predicting Binding Free Energies: Frontiers and Benchmarks

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Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

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Combining SFCscore with Random Forests leads to improved affinity prediction for protein-ligand complexes

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A geometric graph-based deep learning model for drug-target affinity prediction

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DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity

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PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction

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Binding mode information improves fragment docking

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DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism

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Distance plus attention for binding affinity prediction

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Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4

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Interaction Analysis by Fragment Molecular Orbital Method for Drug Discovery Research

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Comparability of Mixed IC50 Data – A Statistical Analysis

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Towards Ligand Docking Including Explicit Interface Water Molecules

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istar: A Web Platform for Large-Scale Protein-Ligand Docking

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Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

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A Teach-Discover-Treat Application of ZincPharmer: An Online Interactive Pharmacophore Modeling and Virtual Screening Tool

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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

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Edge-enhanced interaction graph network for protein-ligand binding affinity prediction

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A Novel Atom Pair Attention Methodology for Molecular Representation Learning

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Pharmacophore modeling: advances, limitations, and current utility in drug discovery

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Convolutional Neural Network-based Virtual Screening

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Scoring Functions for Prediction of Protein-Ligand Interactions

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A Novel Atom Pair Attention Methodology for MolecularRepresentation Learning

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A non-redundant benchmark for symmetric protein docking

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Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

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OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity Based on Residue-Atom Contacting Shells

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Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

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RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

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Solvated interaction energy: from small-molecule to antibody drug design

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Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets

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Multipose Binding in Molecular Docking

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Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design

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Protein–Ligand Docking in the Machine-Learning Era

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Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks

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Faculty Opinions recommendation of Updates to binding MOAD (mother of all databases): polypharmacology tools and their utility in drug repurposing.

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Scoring Functions of Protein-Ligand Interactions

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Scoring Functions of Protein-Ligand Interactions

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Computational Chemistry And Drug Discovery: A Call To Action

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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

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An Overview on Pharmacophore: Their significance and importance for the activity of Drug Design

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Développement et application d’une approche de docking par fragments pour modéliser les interactions entre protéines et ARN simple-brin

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Design and application of deep learning methods to structure-based drug design

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