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NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

https://doi.org/10.1021/ci100244v
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Integrated Molecular Modeling and Machine Learning for Drug Design
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Physics-Inspired Accuracy Estimator for Model-Docked Ligand Complexes
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Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease
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Iterative Knowledge-Based Scoring Function for Protein–Ligand Interactions by Considering Binding Affinity Information
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SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction
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SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein–Ligand Interaction Modeling for METTL3
inferred← citeshttps://doi.org/10.1021/acsomega.9b01997openalex-snapshot
OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction
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A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing
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NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
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Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening
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Improvement of Virtual Screening Results by Docking Data Feature Analysis
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Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
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Classification of Current Scoring Functions
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One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery
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A practical guide to machine-learning scoring for structure-based virtual screening
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Interfering with the high-affinity interaction between wheat amylase trypsin inhibitor CM3 and toll-like receptor 4: in silico and biosensor-based studies
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Evaluating protein binding interfaces with transformer networks
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Biomolecular modeling thrives in the age of technology
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MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery
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Structure and dynamics in drug discovery
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Leveraging Machine Learning for Network Pharmacology
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Docking and Virtual Screening
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Drug repositioning by structure-based virtual screening
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Biomolecular proteomics discloses ATP synthase as the main target of the natural glycoside deglucoruscin
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Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease
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A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
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Modeling protein–ligand interactions for drug discovery in the era of deep learning
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Remodelling structure-based drug design using machine learning
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Synthesis andin silicostudy of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2
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Insight into human protease activated receptor-1 as anticancer target by molecular modelling
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Computational representations of protein–ligand interfaces for structure-based virtual screening
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Advances in the discovery of new chemotypes through ultra-large library docking
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Beware of the generic machine learning-based scoring functions in structure-based virtual screening
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Improving structure-based virtual screening performance via learning from scoring function components
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Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction
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Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term
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A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
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Leveraging scaffold information to predict protein–ligand binding affinity with an empirical graph neural network
inferred← citeshttps://doi.org/10.1093/bib/bbae145openalex-snapshot
A new paradigm for applying deep learning to protein–ligand interaction prediction
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Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
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PDB-wide collection of binding data: current status of the PDBbind database
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Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1093/bioinformatics/btz183openalex-snapshot
Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
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Trypanocidal activity of the anthocyanidin delphinidin, a non-competitive inhibitor of arginine kinase
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PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks
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MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery
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Fully flexible molecular alignment enables accurate ligand structure modelling
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GDEE: A Structure-Based Platform for Gene Discovery and Enzyme Engineering
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Structural Learning of Proteins Using Graph Convolutional Neural Networks
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RFL-Score: Random Forest with Lasso Scoring Function for Protein-Ligand Molecular Docking
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CGDeepAff: Deep Learning-Based Approach for Protein-Ligand Binding Affinity Estimation Using CNN-GRU
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Virtual drug screening using neural networks
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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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Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction
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Farnesyl Diphosphate Synthase Inhibitors fromIn SilicoScreening
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Machine‐Learning Techniques Applied to Antibacterial Drug Discovery
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The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools
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Modelling, substrate docking and mutational analysis identify residues essential for function and specificity of the major fungal purine transporter AzgA
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Potential HIV-1 fusion inhibitors mimicking gp41-specific broadly neutralizing antibody 10E8: In silico discovery and prediction of antiviral potency
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Boosted neural networks scoring functions for accurate ligand docking and ranking
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Molecular Docking Study of Antibiotics, Anti-Inflammatory Drugs and [Eu(TTA)3⋅AMX] Complex as COVID-19 Biomarker through Interaction of Its Main Protease (Mpro)
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The Art and Science of Molecular Docking
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Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches
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Virtual Interactomics of Proteins from Biochemical Standpoint
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Métodos híbridos em docagem molecular: implementação, validação e aplicação
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BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
inferred← citeshttps://doi.org/10.1186/s12859-022-04762-3openalex-snapshot
Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein–ligand affinity prediction
inferred← citeshttps://doi.org/10.1186/s12859-022-04767-yopenalex-snapshot
Explainable deep drug–target representations for binding affinity prediction
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A comprehensive survey of scoring functions for protein docking models
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A geometric graph-based deep learning model for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1186/s13321-017-0237-8openalex-snapshot
Scoria: a Python module for manipulating 3D molecular data
inferred← citeshttps://doi.org/10.1186/s13321-020-00429-4openalex-snapshot
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization
inferred← citeshttps://doi.org/10.1186/s13321-021-00522-2openalex-snapshot
GNINA 1.0: molecular docking with deep learning
inferred← citeshttps://doi.org/10.1186/s13321-021-00560-wopenalex-snapshot
The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
inferred← citeshttps://doi.org/10.1186/s13321-023-00795-9openalex-snapshot
Structure-based, deep-learning models for protein-ligand binding affinity prediction
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A beginner’s approach to deep learning applied to VS and MD techniques
inferred← citeshttps://doi.org/10.1186/s13321-026-01287-2openalex-snapshot
DockM8: an all-in-one open-source platform for consensus virtual screening in drug design
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Prospects of Indole derivatives as methyl transfer inhibitors: antimicrobial resistance managers
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Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
inferred← citeshttps://doi.org/10.1248/cpb.c19-00443openalex-snapshot
Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1002397openalex-snapshot
AutoClickChem: Click Chemistry in Silico
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