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Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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inferred← citeshttps://doi.org/10.1007/978-981-96-4154-3_16openalex-snapshot
Accomplishment of Toxicity Prediction by CADD Tools
inferred← citeshttps://doi.org/10.1007/978-981-99-7074-2_5openalex-snapshot
Reconciling Inconsistent Molecular Structures from Biochemical Databases
inferred← citeshttps://doi.org/10.1007/s00044-014-1189-4openalex-snapshot
Mixed 2D–3D-LQTA-QSAR study of a series of Plasmodium falciparum dUTPase inhibitors
inferred← citeshttps://doi.org/10.1007/s00204-021-03056-6openalex-snapshot
Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates
inferred← citeshttps://doi.org/10.1007/s00204-022-03376-1openalex-snapshot
In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept
inferred← citeshttps://doi.org/10.1007/s00216-024-05424-4openalex-snapshot
Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification — is it time for HT-EDA?
inferred← citeshttps://doi.org/10.1007/s00894-014-2322-5openalex-snapshot
Industrial applications of in silico ADMET
inferred← citeshttps://doi.org/10.1007/s00894-022-05359-6openalex-snapshot
On the ability of machine learning methods to discover novel scaffolds
inferred← citeshttps://doi.org/10.1007/s10619-017-7209-xopenalex-snapshot
Crowd enabled curation and querying of large and noisy text mined protein interaction data
inferred← citeshttps://doi.org/10.1007/s10822-013-9664-4openalex-snapshot
From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions
inferred← citeshttps://doi.org/10.1007/s10822-015-9860-5openalex-snapshot
Activity, assay and target data curation and quality in the ChEMBL database
inferred← citeshttps://doi.org/10.1007/s10822-020-00310-4openalex-snapshot
Revealing cytotoxic substructures in molecules using deep learning
inferred← citeshttps://doi.org/10.1007/s10822-025-00671-8openalex-snapshot
Developing a predictive QSAR model for FGFR-1 inhibitors: integrating computational and experimental validation
inferred← citeshttps://doi.org/10.1007/s10822-026-00833-2openalex-snapshot
Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights
inferred← citeshttps://doi.org/10.1007/s10822-026-00854-xopenalex-snapshot
A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models
inferred← citeshttps://doi.org/10.1007/s10822-026-00876-5openalex-snapshot
Artificial intelligence in impurity prediction: current landscape, challenges, and future directions
inferred← citeshttps://doi.org/10.1007/s10822-026-00919-xopenalex-snapshot
Computational identification of potential VEGFR2 inhibitors using integrated pharmacophore modeling, QSAR analysis, molecular docking, dynamics simulation, and virtual screening followed by in vitro bioassays
inferred← citeshttps://doi.org/10.1007/s10822-026-00955-7openalex-snapshot
A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery
inferred← citeshttps://doi.org/10.1007/s10910-020-01184-5openalex-snapshot
Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes
inferred← citeshttps://doi.org/10.1007/s11030-012-9384-zopenalex-snapshot
Consensus QSAR model for identifying novel H5N1 inhibitors
inferred← citeshttps://doi.org/10.1007/s11030-014-9513-yopenalex-snapshot
Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?
inferred← citeshttps://doi.org/10.1007/s11030-022-10386-9openalex-snapshot
The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design
inferred← citeshttps://doi.org/10.1007/s11030-022-10433-5openalex-snapshot
Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches
inferred← citeshttps://doi.org/10.1007/s11030-025-11196-5openalex-snapshot
Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling
inferred← citeshttps://doi.org/10.1007/s11030-025-11200-yopenalex-snapshot
Exploring Lamiaceae diterpenoids as potential multitarget therapeutics for leishmaniasis and chagas disease
inferred← citeshttps://doi.org/10.1007/s11030-025-11454-6openalex-snapshot
Deep learning-enhanced QSAR modeling for predicting developmental neurotoxicity based on molecular initiating events from adverse outcome pathways
inferred← citeshttps://doi.org/10.1007/s11030-026-11614-2openalex-snapshot
Acetylcholinesterase inhibitory activity of phthalimide derivatives as anti-alzheimer agents: QSAR, ARKA, Hybrid ARKA-RASAR, virtual screening, molecular docking and ADMET studies
inferred← citeshttps://doi.org/10.1007/s11030-026-11674-4openalex-snapshot
From structural features to mouse acute intraperitoneal toxicity prediction: a triple computational toxicology approach for safety assessment of flavonoids
inferred← citeshttps://doi.org/10.1007/s11094-025-03286-3openalex-snapshot
Assessment of the Efficiency of Selecting Promising Compounds During Virtual Screening Based on Various Estimations of Drug-Likeness
inferred← citeshttps://doi.org/10.1007/s11095-012-0935-xopenalex-snapshot
Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions
inferred← citeshttps://doi.org/10.1007/s11095-013-1023-6openalex-snapshot
The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding
inferred← citeshttps://doi.org/10.1007/s11095-016-2029-7openalex-snapshot
The Next Era: Deep Learning in Pharmaceutical Research
inferred← citeshttps://doi.org/10.1007/s11101-025-10207-5openalex-snapshot
Isoquinoline alkaloids from Amaryllidaceae plants as potential agents against neglected tropical diseases: a review from 2019 to 2025
inferred← citeshttps://doi.org/10.1007/s11172-017-1954-xopenalex-snapshot
Computational platform Way2Drug: from the prediction of biological activity to drug repurposing
inferred← citeshttps://doi.org/10.1007/s11172-019-2683-0openalex-snapshot
Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations
inferred← citeshttps://doi.org/10.1007/s11224-012-0195-8openalex-snapshot
QSAR analysis of poliovirus inhibition by dual combinations of antivirals
inferred← citeshttps://doi.org/10.1007/s11224-015-0715-4openalex-snapshot
Computational assessment of environmental hazards of nitroaromatic compounds: influence of the type and position of aromatic ring substituents on toxicity
inferred← citeshttps://doi.org/10.1007/s11696-026-05026-7openalex-snapshot
Discovery of potent VEGFR2 inhibitors as anticancer target, using pharmacophore based virtual screening, molecular docking and molecular dynamics simulation
inferred← citeshttps://doi.org/10.1007/s12247-026-10759-9openalex-snapshot
Integrative QSAR-GaMD Approach Identifies Stable Repurposed Inhibitors Targeting Dengue Virus Proteins
inferred← citeshttps://doi.org/10.1007/s40203-022-00133-1openalex-snapshot
Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents
inferred← citeshttps://doi.org/10.1007/s40264-018-0688-5openalex-snapshot
Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions
inferred← citeshttps://doi.org/10.1007/s40726-026-00401-0openalex-snapshot
Recent Advances in Deep Learning-Based Toxicity Prediction of Emerging Organic Pollutants in Freshwater Ecosystems
inferred← citeshttps://doi.org/10.1007/s41061-021-00349-3openalex-snapshot
Web-Based Quantitative Structure–Activity Relationship Resources Facilitate Effective Drug Discovery
inferred← citeshttps://doi.org/10.1007/s42250-025-01239-7openalex-snapshot
Predicting Pesticide LC50 Toxicity Using Convolutional Neural Networks and Dragonfly-Support Vector Machine Methods with Quantitative Structure-Activity Relationship and 2D Molecular Fingerprints: A Case Study on Oncorhynchus Mykiss
inferred← citeshttps://doi.org/10.1016/b978-0-08-100098-4.00005-3openalex-snapshot
Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1016/b978-0-12-374993-2.10009-3openalex-snapshot
Environmental Toxicology and Hazardous Waste Characterization
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.12347-9openalex-snapshot
Matched Molecular Pair Analysis
inferred← citeshttps://doi.org/10.1016/b978-0-12-809633-8.20192-1openalex-snapshot
Public Chemical Databases
inferred← citeshttps://doi.org/10.1016/b978-0-12-820546-4.00002-7openalex-snapshot
QSAR: Descriptor calculations, model generation, validation and their application
inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00017-5openalex-snapshot
Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
inferred← citeshttps://doi.org/10.1016/b978-0-12-824156-1.00014-5openalex-snapshot
QSAR approach for combating cancer cells
inferred← citeshttps://doi.org/10.1016/b978-0-323-89775-4.00009-2openalex-snapshot
Pharmacophore modeling and its applications
inferred← citeshttps://doi.org/10.1016/b978-0-323-91294-5.00008-7openalex-snapshot
Structure-related relationship: Plant-derived antidiabetic compounds
inferred← citeshttps://doi.org/10.1016/b978-0-443-15339-6.00003-5openalex-snapshot
Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity
inferred← citeshttps://doi.org/10.1016/b978-0-443-15339-6.00016-3openalex-snapshot
QSAR tools for toxicity prediction in risk assessment—Comparative analysis
inferred← citeshttps://doi.org/10.1016/b978-0-443-15339-6.00036-9openalex-snapshot
QSAR facilitating safety evaluation and risk assessment
inferred← citeshttps://doi.org/10.1016/b978-0-443-15339-6.00039-4openalex-snapshot
QSAR models for predicting cardiac toxicity of drugs
inferred← citeshttps://doi.org/10.1016/b978-0-443-18638-7.00029-3openalex-snapshot
Quantitative structure-activity relationships (QSARs) in medicinal chemistry
inferred← citeshttps://doi.org/10.1016/b978-0-443-31550-3.00001-9openalex-snapshot
Introduction to bioinformatics in microbial biotechnology
inferred← citeshttps://doi.org/10.1016/b978-0-443-44415-9.00018-1openalex-snapshot
Generative artificial intelligence for de novo drug design
inferred← citeshttps://doi.org/10.1016/b978-0-443-44631-3.00008-1openalex-snapshot
Immune cells and nanotechnology: Innovations in therapy
inferred← citeshttps://doi.org/10.1016/bs.apcsb.2018.04.001openalex-snapshot
The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD)
inferred← citeshttps://doi.org/10.1016/bs.armc.2019.06.004openalex-snapshot
Shortcuts to schistosomiasis drug discovery: The state-of-the-art
inferred← citeshttps://doi.org/10.1016/bs.mim.2024.05.008openalex-snapshot
AI-driven microbial medicine development
inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.005openalex-snapshot
Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models
inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.006openalex-snapshot
In silico methods for predicting drug–drug interactions with cytochrome P-450s, transporters and beyond
inferred← citeshttps://doi.org/10.1016/j.ailsci.2021.100008openalex-snapshot
An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition
inferred← citeshttps://doi.org/10.1016/j.ailsci.2021.100013openalex-snapshot
BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees
inferred← citeshttps://doi.org/10.1016/j.ailsci.2021.100028openalex-snapshot
Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals
inferred← citeshttps://doi.org/10.1016/j.ailsci.2026.100183openalex-snapshot
Artificial intelligence in drug discovery: Integrating data, models, and mechanisms for next-generation therapeutics
inferred← citeshttps://doi.org/10.1016/j.algal.2026.104997openalex-snapshot
Exploring predicted oncogenic-target inhibition and caspase sparing in cross-phyla seaweed metabolites: A machine learning and in silico approach
inferred← citeshttps://doi.org/10.1016/j.antiviral.2010.02.456openalex-snapshot
QSAR Analysis of Poliovirus Inhibition By Dual Combinations of Antivirals
inferred← citeshttps://doi.org/10.1016/j.antiviral.2023.105620openalex-snapshot
Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules
inferred← citeshttps://doi.org/10.1016/j.antiviral.2025.106184openalex-snapshot
Chemical arsenal for helicase Hunters: Striking the toughest targets in antiviral research
inferred← citeshttps://doi.org/10.1016/j.aquatox.2021.105962openalex-snapshot
Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata
inferred← citeshttps://doi.org/10.1016/j.aquatox.2024.107136openalex-snapshot
Elucidation of molecular mechanisms involved in tadpole toxicity employing QSTR and q-RASAR approach
inferred← citeshttps://doi.org/10.1016/j.bmc.2013.03.032openalex-snapshot
Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods
inferred← citeshttps://doi.org/10.1016/j.bmcl.2012.12.067openalex-snapshot
A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors
inferred← citeshttps://doi.org/10.1016/j.bmcl.2017.04.010openalex-snapshot
Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum
inferred← citeshttps://doi.org/10.1016/j.cbi.2016.06.011openalex-snapshot
Multi-level structure-based pharmacophore modelling of caspase-3-non-peptide complexes: Extracting essential pharmacophore features and its application to virtual screening
inferred← citeshttps://doi.org/10.1016/j.chembiol.2011.12.007openalex-snapshot
Established and Emerging Trends in Computational Drug Discovery in the Structural Genomics Era
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