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The Experimental Uncertainty of Heterogeneous Public Ki Data
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Outgoing claims: the papers this work cites, builds on, or argues with.
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The IUPAC Compendium of Chemical Terminology
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Estimating Error Rates in Bioactivity Databases
inferred← citeshttps://doi.org/10.1021/ci4004249openalex-snapshot
Check Your Confidence: Size Really Does Matter
inferred← citeshttps://doi.org/10.1021/ci400709dopenalex-snapshot
Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
inferred← citeshttps://doi.org/10.1021/ci500080qopenalex-snapshot
Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set
inferred← citeshttps://doi.org/10.1021/ci5005256openalex-snapshot
VAMMPIRE-LORD: A Web Server for Straightforward Lead Optimization Using Matched Molecular Pairs
inferred← citeshttps://doi.org/10.1021/ci5005509openalex-snapshot
Influence of Search Parameters and Criteria on Compound Selection, Promiscuity, and Pan Assay Interference Characteristics
inferred← citeshttps://doi.org/10.1021/ci500731aopenalex-snapshot
Classification of Current Scoring Functions
inferred← citeshttps://doi.org/10.1021/jm3003069openalex-snapshot
Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data
inferred← citeshttps://doi.org/10.1021/jm500317aopenalex-snapshot
Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty
inferred← citeshttps://doi.org/10.1038/s41467-023-39856-wopenalex-snapshot
Sequence-based drug design as a concept in computational drug design
inferred← citeshttps://doi.org/10.1038/s41467-024-45431-8openalex-snapshot
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
inferred← citeshttps://doi.org/10.1038/s41570-023-00552-4openalex-snapshot
The experiment is the limit
inferred← citeshttps://doi.org/10.1038/s41573-023-00774-7openalex-snapshot
Artificial intelligence for natural product drug discovery
inferred← citeshttps://doi.org/10.1038/s41586-026-11013-5openalex-snapshot
Development of a random background to understand ligand optimization
inferred← citeshttps://doi.org/10.1038/s41597-024-03582-9openalex-snapshot
A compound-target pairs dataset: differences between drugs, clinical candidates and other bioactive compounds
inferred← citeshttps://doi.org/10.1038/s42004-023-01019-9openalex-snapshot
The maximal and current accuracy of rigorous protein-ligand binding free energy calculations
inferred← citeshttps://doi.org/10.1038/s43588-024-00699-0openalex-snapshot
The future of machine learning for small-molecule drug discovery will be driven by data
inferred← citeshttps://doi.org/10.1038/s44386-025-00031-4openalex-snapshot
Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches
inferred← citeshttps://doi.org/10.1038/sdata.2015.32openalex-snapshot
A large-scale crop protection bioassay data set
inferred← citeshttps://doi.org/10.1039/c4cp01388copenalex-snapshot
Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set
inferred← citeshttps://doi.org/10.1039/c4ib00175copenalex-snapshot
Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features
inferred← citeshttps://doi.org/10.1039/c4md00216dopenalex-snapshot
Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects
inferred← citeshttps://doi.org/10.1039/c8sc04175jopenalex-snapshot
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
inferred← citeshttps://doi.org/10.1039/d4fd00091aopenalex-snapshot
Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery
inferred← citeshttps://doi.org/10.1073/pnas.2112621118openalex-snapshot
Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1078407openalex-snapshot
PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach
inferred← citeshttps://doi.org/10.1080/1062936x.2017.1351391openalex-snapshot
Improved correlation between animal and human potency of non-steroidal anti-inflammatory drugs using quantitative structure–activity relationships (QSARs)
inferred← citeshttps://doi.org/10.1080/17460441.2026.2624023openalex-snapshot
Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma
inferred← citeshttps://doi.org/10.1088/1752-7155/8/4/046001openalex-snapshot
Precursors for cytochrome P450 profiling breath tests from anin silicoscreening approach
inferred← citeshttps://doi.org/10.1093/bib/bbv037openalex-snapshot
Building a virtual ligand screening pipeline using free software: a survey
inferred← citeshttps://doi.org/10.1093/bioinformatics/btt540openalex-snapshot
Shaping the interaction landscape of bioactive molecules
inferred← citeshttps://doi.org/10.1093/bioinformatics/btz293openalex-snapshot
A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery
inferred← citeshttps://doi.org/10.1093/nar/gkt1031openalex-snapshot
The ChEMBL bioactivity database: an update
inferred← citeshttps://doi.org/10.1101/2020.03.18.996538openalex-snapshot
A Novel Machine Learning Approach Uncovers New and Distinctive Inhibitors for Cyclin-Dependent Kinase 9
inferred← citeshttps://doi.org/10.1101/2020.06.01.128181openalex-snapshot
Leveraging non-structural data to predict structures of protein–ligand complexes
inferred← citeshttps://doi.org/10.1101/2020.06.12.135939openalex-snapshot
Systematic analysis of protein targets associated with adverse events of drugs from clinical trials and post-marketing reports
inferred← citeshttps://doi.org/10.1101/2022.03.26.485909openalex-snapshot
Drug design and repurposing with a sequence-to-drug paradigm
inferred← citeshttps://doi.org/10.1101/2024.07.01.600773openalex-snapshot
Leveraging machine learning to streamline the development of liposomal drug delivery systems
inferred← citeshttps://doi.org/10.1101/2024.09.10.612176openalex-snapshot
Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance
inferred← citeshttps://doi.org/10.1101/2025.08.17.670706openalex-snapshot
AMODO-EO: A Practical Framework for Adaptive Objective Discovery in Multi-Objective Drug Optimization
inferred← citeshttps://doi.org/10.1124/mol.112.084152openalex-snapshot
Structure-Based Identification of OATP1B1/3 Inhibitors
inferred← citeshttps://doi.org/10.1126/sciadv.adx6373openalex-snapshot
Atom-level machine learning of protein-glycan interactions and cross-chiral recognition in glycobiology
inferred← citeshttps://doi.org/10.1146/annurev-biophys-083012-130318openalex-snapshot
Entropy-Enthalpy Compensation: Role and Ramifications in Biomolecular Ligand Recognition and Design
inferred← citeshttps://doi.org/10.1186/1471-2164-15-380openalex-snapshot
Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks
inferred← citeshttps://doi.org/10.1186/1758-2946-5-33openalex-snapshot
Inferring multi-target QSAR models with taxonomy-based multi-task learning
inferred← citeshttps://doi.org/10.1186/1758-2946-6-35openalex-snapshot
Proteochemometric modeling in a Bayesian framework
inferred← citeshttps://doi.org/10.1186/1758-2946-6-s1-o6openalex-snapshot
Limits to molecular matched-pair analysis: the experimental uncertainty case
inferred← citeshttps://doi.org/10.1186/s12859-017-1586-zopenalex-snapshot
MOST: most-similar ligand based approach to target prediction
inferred← citeshttps://doi.org/10.1186/s13321-014-0049-zopenalex-snapshot
Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling
inferred← citeshttps://doi.org/10.1186/s13321-017-0226-yopenalex-snapshot
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
inferred← citeshttps://doi.org/10.1186/s13321-017-0232-0openalex-snapshot
Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
inferred← citeshttps://doi.org/10.1186/s13321-020-00438-3openalex-snapshot
Quantitative prediction of selectivity between the A1 and A2A adenosine receptors
inferred← citeshttps://doi.org/10.1186/s13321-021-00525-zopenalex-snapshot
Nonadditivity in public and inhouse data: implications for drug design
inferred← citeshttps://doi.org/10.1186/s13321-021-00539-7openalex-snapshot
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
inferred← citeshttps://doi.org/10.1186/s13321-021-00564-6openalex-snapshot
Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion
inferred← citeshttps://doi.org/10.1186/s13321-021-00571-7openalex-snapshot
The effect of noise on the predictive limit of QSAR models
inferred← citeshttps://doi.org/10.1186/s13321-021-00574-4openalex-snapshot
Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development
inferred← citeshttps://doi.org/10.1186/s13321-022-00607-6openalex-snapshot
Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs
inferred← citeshttps://doi.org/10.1208/s12248-017-0172-7openalex-snapshot
Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling
inferred← citeshttps://doi.org/10.1371/journal.pone.0061007openalex-snapshot
Comparability of Mixed IC50 Data – A Statistical Analysis
inferred← citeshttps://doi.org/10.1371/journal.pone.0341501openalex-snapshot
Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.
inferred← citeshttps://doi.org/10.1371/journal.pone.0351880openalex-snapshot
A multi-modal co-attention model for accurate drug-target interaction prediction
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