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NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Binding MOAD (Mother Of All Databases)
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Protein–ligand docking: Current status and future challenges
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NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
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Molecular Dynamics: Survey of Methods for Simulating the Activity of Proteins
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FEP-Guided Selection of Bicyclic Heterocycles in Lead Optimization for Non-Nucleoside Inhibitors of HIV-1 Reverse Transcriptase
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The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures
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The PDBbind Database: Methodologies and Updates
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Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
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Newton-Type Minimization via the Lanczos Method
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Molecular dynamics simulations and drug discovery
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Random Forest Refinement of Pairwise Potentials for Protein–Ligand Decoy Detection
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00387openalex-snapshot
Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00645openalex-snapshot
Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00714openalex-snapshot
Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
inferred← citeshttps://doi.org/10.1021/acs.jctc.1c00503openalex-snapshot
FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection
inferred← citeshttps://doi.org/10.1021/acs.jctc.3c00814openalex-snapshot
Integrated Molecular Modeling and Machine Learning for Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jctc.4c01373openalex-snapshot
Physics-Inspired Accuracy Estimator for Model-Docked Ligand Complexes
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01830openalex-snapshot
InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c00991openalex-snapshot
Boosting Protein–Ligand Binding Pose Prediction and Virtual Screening Based on Residue–Atom Distance Likelihood Potential and Graph Transformer
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c02096openalex-snapshot
Discovery of Two Structurally Distinct Classes of Inhibitors Targeting the Nuclease MUS81 and Enhancing Efficacy of Chemotherapy in Cancer Cells
inferred← citeshttps://doi.org/10.1021/acs.jpcb.0c02322openalex-snapshot
RNAPosers: Machine Learning Classifiers for Ribonucleic Acid–Ligand Poses
inferred← citeshttps://doi.org/10.1021/acs.jpcb.3c04421openalex-snapshot
Iterative Knowledge-Based Scoring Function for Protein–Ligand Interactions by Considering Binding Affinity Information
inferred← citeshttps://doi.org/10.1021/acscatal.4c04660openalex-snapshot
A Machine Learning-Guided Approach to Navigate the Substrate Activity Scope of Galactose Oxidase: Application in the Conversion of Pharmaceutically Relevant Bulky Secondary Alcohols
inferred← citeshttps://doi.org/10.1021/acscentsci.5c01473openalex-snapshot
From Prompt to Drug: Toward Pharmaceutical Superintelligence
inferred← citeshttps://doi.org/10.1021/acscentsci.8b00507openalex-snapshot
PotentialNet for Molecular Property Prediction
inferred← citeshttps://doi.org/10.1021/acsomega.3c05931openalex-snapshot
Expanding Training Data for Structure-Based Receptor–Ligand Binding Affinity Regression through Imputation of Missing Labels
inferred← citeshttps://doi.org/10.1021/acsomega.5c00538openalex-snapshot
SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein–Ligand Interaction Modeling for METTL3
inferred← citeshttps://doi.org/10.1021/acsomega.7b01194openalex-snapshot
A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions
inferred← citeshttps://doi.org/10.1021/ci200617dopenalex-snapshot
Ligand-Based Virtual Screening Approach Using a New Scoring Function
inferred← citeshttps://doi.org/10.1021/ci400042yopenalex-snapshot
Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening
inferred← citeshttps://doi.org/10.1021/ci500028uopenalex-snapshot
Improvement of Virtual Screening Results by Docking Data Feature Analysis
inferred← citeshttps://doi.org/10.1021/ci500091ropenalex-snapshot
Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
inferred← citeshttps://doi.org/10.1021/ci500486yopenalex-snapshot
Mechanism-Based Discovery of Novel Substrates of Haloalkane Dehalogenases Using in Silico Screening
inferred← citeshttps://doi.org/10.1021/ci500731aopenalex-snapshot
Classification of Current Scoring Functions
inferred← citeshttps://doi.org/10.1038/s41401-022-00996-2openalex-snapshot
DrugRep: an automatic virtual screening server for drug repurposing
inferred← citeshttps://doi.org/10.1038/s41467-023-37714-3openalex-snapshot
Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity
inferred← citeshttps://doi.org/10.1038/s41467-025-61745-7openalex-snapshot
ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance
inferred← citeshttps://doi.org/10.1038/s41573-023-00832-0openalex-snapshot
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
inferred← citeshttps://doi.org/10.1038/s41587-026-03217-xopenalex-snapshot
AI-enhanced adaptive virtual screening of large libraries for ligand discovery
inferred← citeshttps://doi.org/10.1038/s41596-023-00885-wopenalex-snapshot
A practical guide to machine-learning scoring for structure-based virtual screening
inferred← citeshttps://doi.org/10.1038/s41596-024-00995-zopenalex-snapshot
Integrating genome-wide CRISPR screens and in silico drug profiling for targeted antidote development
inferred← citeshttps://doi.org/10.1038/s41598-023-29996-wopenalex-snapshot
Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools
inferred← citeshttps://doi.org/10.1038/s41598-025-20634-1openalex-snapshot
Integrating machine learning and protein–ligand interaction profiling for the discovery of METTL3 inhibitors
inferred← citeshttps://doi.org/10.1038/s42004-025-01844-0openalex-snapshot
Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations
inferred← citeshttps://doi.org/10.1038/s42256-020-0152-yopenalex-snapshot
Predicting drug–protein interaction using quasi-visual question answering system
inferred← citeshttps://doi.org/10.1038/srep46710openalex-snapshot
Performance of machine-learning scoring functions in structure-based virtual screening
inferred← citeshttps://doi.org/10.1039/c7sc02664aopenalex-snapshot
MoleculeNet: a benchmark for molecular machine learning
inferred← citeshttps://doi.org/10.1039/c9ra01007fopenalex-snapshot
Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease
inferred← citeshttps://doi.org/10.1039/d2sc06576bopenalex-snapshot
MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions
inferred← citeshttps://doi.org/10.1039/d3dd00110eopenalex-snapshot
Machine learning-augmented docking. 1. CYP inhibition prediction
inferred← citeshttps://doi.org/10.1039/d3sc02044dopenalex-snapshot
A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
inferred← citeshttps://doi.org/10.1039/d5cs00415bopenalex-snapshot
Modeling protein–ligand interactions for drug discovery in the era of deep learning
inferred← citeshttps://doi.org/10.1039/d5sc00451aopenalex-snapshot
Spectra-descriptor-based machine learning for predicting protein–ligand interactions
inferred← citeshttps://doi.org/10.1073/pnas.2000585117openalex-snapshot
Machine learning classification can reduce false positives in structure-based virtual screening
inferred← citeshttps://doi.org/10.1073/pnas.2322923121openalex-snapshot
Targeting the Plasmodium falciparum UCHL3 ubiquitin hydrolase using chemically constrained peptides
inferred← citeshttps://doi.org/10.1080/07391102.2018.1552623openalex-snapshot
Thiophenacetamide as a potential modulator to NF-κB: structure and dynamics study using in silico and molecular biology assays
inferred← citeshttps://doi.org/10.1080/07391102.2020.1792989openalex-snapshot
Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease
inferred← citeshttps://doi.org/10.1080/07391102.2021.1905559openalex-snapshot
Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors
inferred← citeshttps://doi.org/10.1080/07391102.2022.2062784openalex-snapshot
In silico design and computational evaluation of novel 2-arylaminopyrimidine-based compounds as potential multi-targeted protein kinase inhibitors: application for the native and mutant (T315I) Bcr-Abl tyrosine kinase
inferred← citeshttps://doi.org/10.1080/07391102.2022.2133010openalex-snapshot
Synthesis andin silicostudy of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2
inferred← citeshttps://doi.org/10.1080/07391102.2022.2136244openalex-snapshot
Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics
inferred← citeshttps://doi.org/10.1080/07391102.2023.2194994openalex-snapshot
Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer
inferred← citeshttps://doi.org/10.1080/07391102.2023.2293276openalex-snapshot
Virtual screening and identification of promising therapeutic compounds against drug-resistant Mycobacterium tuberculosis β-ketoacyl-acyl carrier protein synthase I (KasA)
inferred← citeshttps://doi.org/10.1080/07391102.2024.2332509openalex-snapshot
Identification of putative Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) dual inhibitors for triple-negative breast cancer therapy
inferred← citeshttps://doi.org/10.1080/14756366.2019.1651723openalex-snapshot
Towards discovery of novel scaffold with potent antiangiogenic activity; design, synthesis of pyridazine based compounds, impact of hinge interaction, and accessibility of their bioactive conformation on VEGFR-2 activities
inferred← citeshttps://doi.org/10.1080/17460441.2019.1550482openalex-snapshot
Advances and challenges in drug design against tuberculosis: application of in silico approaches
inferred← citeshttps://doi.org/10.1080/17460441.2021.1929921openalex-snapshot
Computational representations of protein–ligand interfaces for structure-based virtual screening
inferred← citeshttps://doi.org/10.1080/17460441.2021.1979514openalex-snapshot
Guided structure-based ligand identification and design via artificial intelligence modeling
inferred← citeshttps://doi.org/10.1080/17460441.2023.2171984openalex-snapshot
Advances in the discovery of new chemotypes through ultra-large library docking
inferred← citeshttps://doi.org/10.1080/17460441.2024.2349169openalex-snapshot
Recent advances in computational and experimental protein-ligand affinity determination techniques
inferred← citeshttps://doi.org/10.1080/17460441.2024.2367014openalex-snapshot
Artificial intelligence for small molecule anticancer drug discovery
inferred← citeshttps://doi.org/10.1088/2632-2153/ace58copenalex-snapshot
Semi-equivariant conditional normalizing flows, with applications to target-aware molecule generation
inferred← citeshttps://doi.org/10.1093/bfgp/elad037openalex-snapshot
Prediction of drug–protein interaction based on dual channel neural networks with attention mechanism
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