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QSAR − How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

https://doi.org/10.1021/ci050413p
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jcc.540070419openalex-snapshot
Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity
inferredcites →https://doi.org/10.1006/jmbi.1998.2255openalex-snapshot
Characterization of flexible molecules in solution: the RGDW peptide 1 1Edited by B. Honig
inferredcites →https://doi.org/10.1007/bf00124364openalex-snapshot
Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA
inferredcites →https://doi.org/10.1007/s10822-004-4060-8openalex-snapshot
An automated PLS search for biologically relevant QSAR descriptors
inferredcites →https://doi.org/10.1021/c160017a018openalex-snapshot
The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service.
inferredcites →https://doi.org/10.1021/c160017a019openalex-snapshot
A Connectivity Code for Use in Describing Chemical Structures.
inferredcites →https://doi.org/10.1021/ci010073hopenalex-snapshot
QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors
inferredcites →https://doi.org/10.1021/ci010132ropenalex-snapshot
Reoptimization of MDL Keys for Use in Drug Discovery
inferredcites →https://doi.org/10.1021/ci025569topenalex-snapshot
Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins
inferredcites →https://doi.org/10.1021/ci025626iopenalex-snapshot
Assessing Model Fit by Cross-Validation
inferredcites →https://doi.org/10.1021/ci049782wopenalex-snapshot
Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
inferredcites →https://doi.org/10.1021/ci980008gopenalex-snapshot
Evaluation of Quantitative Structure−Activity Relationship Methods for Large-Scale Prediction of Chemicals Binding to the Estrogen Receptor
inferredcites →https://doi.org/10.1021/ci990162iopenalex-snapshot
The Characterization of Chemical Structures Using Molecular Properties. A Survey
inferredcites →https://doi.org/10.1021/ci990307lopenalex-snapshot
Prediction of Physicochemical Parameters by Atomic Contributions
inferredcites →https://doi.org/10.1021/jm000941mopenalex-snapshot
GRid-INdependent Descriptors (GRIND): A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors
inferredcites →https://doi.org/10.1021/jm0105427openalex-snapshot
Antitumor Agents. 213. Modeling of Epipodophyllotoxin Derivatives Using Variable SelectionkNearest Neighbor QSAR Method
inferredcites →https://doi.org/10.1021/jm0497141openalex-snapshot
A Comparison of Methods for Modeling Quantitative Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1021/jm980697nopenalex-snapshot
Robust QSAR Models Using Bayesian Regularized Neural Networks
inferredcites →https://doi.org/10.1021/jp980230oopenalex-snapshot
Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of ALOGP and CLOGP Methods
inferredcites →https://doi.org/10.1023/a:1020869118689openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferredcites →https://doi.org/10.1039/b409865jopenalex-snapshot
Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures
inferredcites →https://doi.org/10.1080/01621459.1988.10478639openalex-snapshot
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
inferredcites →https://doi.org/10.2174/1568026023392922openalex-snapshot
The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/0471266949.bmc002.pub2openalex-snapshot
Recent Advances in Development, Validation, and Exploitation of QSAR Models
inferred← citeshttps://doi.org/10.1002/0471266949.bmc002.pub3openalex-snapshot
Recent Advances in Development, Validation, and Exploitation of QSAR Models
inferred← citeshttps://doi.org/10.1002/9780470915110.ch2openalex-snapshot
In SilicoADME/Tox Predictions
inferred← citeshttps://doi.org/10.1002/9781119564782.ch12openalex-snapshot
Virtual Screening and Bioactivity Modeling for G Protein‐Coupled Receptors
inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/9783527840748.ch21openalex-snapshot
Advances in the Application of In Silico ADMET Models – An Industry Perspective
inferred← citeshttps://doi.org/10.1002/9783527847068.ch08openalex-snapshot
Predicting Selectivity in Asymmetric Catalysis with Machine Learning
inferred← citeshttps://doi.org/10.1002/advs.202307245openalex-snapshot
De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning
inferred← citeshttps://doi.org/10.1002/ange.202218659openalex-snapshot
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
inferred← citeshttps://doi.org/10.1002/anie.202218659openalex-snapshot
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
inferred← citeshttps://doi.org/10.1002/ardp.201100316openalex-snapshot
Diverse Models for the Prediction of HIV Integrase Inhibitory Activity of Substituted Quinolone Carboxylic Acids
inferred← citeshttps://doi.org/10.1002/aris.2009.1440430108openalex-snapshot
Similarity methods in chemoinformatics
inferred← citeshttps://doi.org/10.1002/cem.70047openalex-snapshot
A Method for Measuring Similarity or Distance of Molecular and Arbitrary Graphs Based on a Collection of Topological Indices
inferred← citeshttps://doi.org/10.1002/chem.202202834openalex-snapshot
Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis
inferred← citeshttps://doi.org/10.1002/marc.202200385openalex-snapshot
Automated Design of Li + ‐Conducting Polymer by Quantum‐Inspired Annealing
inferred← citeshttps://doi.org/10.1002/minf.201000019openalex-snapshot
Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization
inferred← citeshttps://doi.org/10.1002/minf.201000160openalex-snapshot
Predictivity of Simulated ADME AutoQSAR Models over Time
inferred← citeshttps://doi.org/10.1002/minf.201100129openalex-snapshot
Existing and Developing Approaches for QSAR Analysis of Mixtures
inferred← citeshttps://doi.org/10.1002/minf.201400122openalex-snapshot
Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems
inferred← citeshttps://doi.org/10.1002/minf.201700040openalex-snapshot
Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain
inferred← citeshttps://doi.org/10.1002/minf.201900031openalex-snapshot
Medicinal Chemistry Aware Database GDBMedChem
inferred← citeshttps://doi.org/10.1002/minf.201900070openalex-snapshot
The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor
inferred← citeshttps://doi.org/10.1002/minf.202300059openalex-snapshot
Compression of molecular fingerprints with autoencoder networks
inferred← citeshttps://doi.org/10.1002/minf.70015openalex-snapshot
Structure–Activity Relationships and Design of Focused Libraries Tailored for Staphylococcus Aureus Inhibition
inferred← citeshttps://doi.org/10.1002/minf.70032openalex-snapshot
Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO 2 Separation Membranes
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_5openalex-snapshot
Similarity Searching Using 2D Structural Fingerprints
inferred← citeshttps://doi.org/10.1007/978-1-62703-050-2_6openalex-snapshot
Prediction of Physicochemical Properties
inferred← citeshttps://doi.org/10.1007/978-1-62703-592-7_15openalex-snapshot
Cheminformatic Analysis of High-Throughput Compound Screens
inferred← citeshttps://doi.org/10.1007/978-3-031-81728-1_12openalex-snapshot
Success Stories of QSAR Methods in Drug Design
inferred← citeshttps://doi.org/10.1007/978-3-032-19536-4_8openalex-snapshot
Small Molecule Drug Discovery Enhanced by Quantum AI
inferred← citeshttps://doi.org/10.1007/978-3-319-08156-4_26openalex-snapshot
Orphan Drug Legislation with Data Fusion Rules Using Multiple Fingerprints Measurements
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_9openalex-snapshot
QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
inferred← citeshttps://doi.org/10.1007/978-94-017-9257-8_11openalex-snapshot
Computational Toxicology in Drug Discovery: Opportunities and Limitations
inferred← citeshttps://doi.org/10.1007/978-981-16-7213-2_73openalex-snapshot
TPE-Lasso-GBDT Method for BV-2 Cell Toxicity Classifier
inferred← citeshttps://doi.org/10.1007/s00706-010-0301-9openalex-snapshot
Probing the structural requirements for antitubercular activity of scalarane derivatives using 2D-QSAR and CoMFA approaches
inferred← citeshttps://doi.org/10.1007/s00726-008-0228-1openalex-snapshot
Gaussian process: an alternative approach for QSAM modeling of peptides
inferred← citeshttps://doi.org/10.1007/s00726-014-1667-5openalex-snapshot
A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction
inferred← citeshttps://doi.org/10.1007/s10822-018-0106-1openalex-snapshot
Sparse QSAR modelling methods for therapeutic and regenerative medicine
inferred← citeshttps://doi.org/10.1007/s10822-020-00300-6openalex-snapshot
Enhancing reaction-based de novo design using a multi-label reaction class recommender
inferred← citeshttps://doi.org/10.1007/s10822-026-00794-6openalex-snapshot
Combined computational and classical medicinal chemistry procedure to disclose novel pyrrole-based compounds as potential antituberculosis agents
inferred← citeshttps://doi.org/10.1007/s10822-026-00816-3openalex-snapshot
ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization
inferred← citeshttps://doi.org/10.1007/s12039-013-0410-1openalex-snapshot
Diverse models for the prediction of CDK4 inhibitory activity of substituted 4-aminomethylene isoquinoline-1, 3-diones
inferred← citeshttps://doi.org/10.1016/b978-0-12-374194-3.00023-8openalex-snapshot
Quantitative Approaches to Structure–Activity Relationships
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/j.bioorg.2015.02.001openalex-snapshot
3D-QSAR and molecular modeling studies on 2,3-dideoxy hexenopyranosid-4-uloses as anti-tubercular agents targeting alpha-mannosidase
inferred← citeshttps://doi.org/10.1016/j.bmc.2026.118694openalex-snapshot
Identification of non-hydroxamate histone deacetylase 8 inhibitors using deep learning-based screening of two-dimensional molecular images
inferred← citeshttps://doi.org/10.1016/j.cclet.2026.112565openalex-snapshot
Multi-modal feature fusion graph neural networks for intelligent classification of pharmaceutical solid forms: Cocrystals, salts, and physical mixtures
inferred← citeshttps://doi.org/10.1016/j.ccst.2025.100447openalex-snapshot
SAGE-Amine: Generative Amine design with multi-property optimization for efficient CO2 capture
inferred← citeshttps://doi.org/10.1016/j.chemphys.2026.113522openalex-snapshot
A machine learning-driven multiscale framework for virtual screening of natural products: Identification of ERK2 inhibitors from the SuperNatural II database
inferred← citeshttps://doi.org/10.1016/j.commatsci.2023.112361openalex-snapshot
Transfer learning for predicting reorganization energy
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108814openalex-snapshot
VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach
inferred← citeshttps://doi.org/10.1016/j.crfs.2026.101404openalex-snapshot
In silico discovery of umami peptides: From sequence-based intelligent models to structure–function mechanistic insight
inferred← citeshttps://doi.org/10.1016/j.dib.2026.113342openalex-snapshot
A dataset of conformer-independent structural molecular property indices for 33,715 organic molecules
inferred← citeshttps://doi.org/10.1016/j.drudis.2022.103356openalex-snapshot
Concepts and applications of chemical fingerprint for hit and lead screening
inferred← citeshttps://doi.org/10.1016/j.ejmech.2017.05.026openalex-snapshot
QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity
inferred← citeshttps://doi.org/10.1016/j.ejmech.2018.08.033openalex-snapshot
Natural products as inhibitors of Leishmania major dihydroorotate dehydrogenase
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126972openalex-snapshot
Multi-task multi-view and iterative error-correcting random forest for acute toxicity prediction
inferred← citeshttps://doi.org/10.1016/j.imu.2023.101424openalex-snapshot
Pharmacophore-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities
inferred← citeshttps://doi.org/10.1016/j.isci.2022.105677openalex-snapshot
Supervised chemical graph mining improves drug-induced liver injury prediction
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2023.131344openalex-snapshot
Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse
inferred← citeshttps://doi.org/10.1016/j.jmgm.2008.01.002openalex-snapshot
The importance of the domain of applicability in QSAR modeling
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.07.005openalex-snapshot
Calculation and application of activity discriminants in lead optimization
inferred← citeshttps://doi.org/10.1016/j.jmgm.2021.107848openalex-snapshot
Machine Learning Transition Temperatures from 2D Structure
inferred← citeshttps://doi.org/10.1016/j.lfs.2025.124180openalex-snapshot
Advances in AI for predicting pharmacological properties of natural medicines
inferred← citeshttps://doi.org/10.1016/j.molstruc.2018.11.019openalex-snapshot
In silico evaluation of 5-hydroxypyrazoles as LSD1 inhibitors based on molecular docking derived descriptors
inferred← citeshttps://doi.org/10.1016/j.tifs.2021.05.031openalex-snapshot
Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects
inferred← citeshttps://doi.org/10.1016/s0079-6468(10)49004-9openalex-snapshot
Computational Analysis of Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.analchem.6c01847openalex-snapshot
Machine Learning-Enhanced DGT Passive Sampling Coupled with Non-Targeted Analysis for High-Throughput Monitoring of Chemical Pollutants in Aquatic Systems
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00065openalex-snapshot
Enlarged Data Sets and Innovative Applicability Domain Characterization Empower ML Models to Reliably Bridge hERG Binding Data Gaps in Diverse Chemicals
inferred← citeshttps://doi.org/10.1021/acs.est.6c03516openalex-snapshot
Confidently Uncertain: Probabilistic Machine Learning to Predict Soil Biotransformation Half-Lives
inferred← citeshttps://doi.org/10.1021/acs.est.6c05993openalex-snapshot
Multimodal Machine Learning Models on Aqueous Direct Photodisappearance Rate Constants of Chemicals Exhibit Previously Unattainable Performance
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00005openalex-snapshot
TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00193openalex-snapshot
Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00622openalex-snapshot
Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00915openalex-snapshot
REINVENT 2.0: An AI Tool for De Novo Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01164openalex-snapshot
Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00317openalex-snapshot
BiasNet: A Model to Predict Ligand Bias Toward GPCR Signaling
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00695openalex-snapshot
Prioritizing Virtual Screening with Interpretable Interaction Fingerprints
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00050openalex-snapshot
Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00393openalex-snapshot
Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01923openalex-snapshot
Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00366openalex-snapshot
Prediction and Interpretation Microglia Cytotoxicity by Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00676openalex-snapshot
Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00724openalex-snapshot
High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02120openalex-snapshot
Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00720openalex-snapshot
Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01982openalex-snapshot
Deep Learning vs Classical Methods in Potency and ADME Prediction: Insights from a Computational Blind Challenge
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00109openalex-snapshot
bioassayR: Cross-Target Analysis of Small Molecule Bioactivity
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00572openalex-snapshot
Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00831openalex-snapshot
K-UID: A Scale-Invariant Topological Address System for Noncovalent Interaction Classification Across Molecular Scales
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00315openalex-snapshot
Developing Collaborative QSAR Models Without Sharing Structures
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00902openalex-snapshot
Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00526openalex-snapshot
Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00927openalex-snapshot
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c00254openalex-snapshot
Quantitative Structure–Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b00959openalex-snapshot
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
inferred← citeshttps://doi.org/10.1021/acs.jnatprod.3c00879openalex-snapshot
PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning
inferred← citeshttps://doi.org/10.1021/acs.jpca.4c04121openalex-snapshot
Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks
inferred← citeshttps://doi.org/10.1021/acs.jpclett.5c01940openalex-snapshot
Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.4c00086openalex-snapshot
POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles
inferred← citeshttps://doi.org/10.1021/acschemneuro.9b00650openalex-snapshot
The Brain Exposure Efficiency (BEE) Score
inferred← citeshttps://doi.org/10.1021/acsomega.2c02554openalex-snapshot
LGB-Stack : Stacked Generalization with LightGBM for Highly Accurate Predictions of Polymer Bandgap
inferred← citeshttps://doi.org/10.1021/acsomega.3c02803openalex-snapshot
Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders
inferred← citeshttps://doi.org/10.1021/acsomega.5c07323openalex-snapshot
Development of Pesticide-Likeness Scores and Models for Predicting Pesticide Activity of Molecular Scaffolds with Machine Learning
inferred← citeshttps://doi.org/10.1021/acsomega.6c04211openalex-snapshot
Chemically Interpretable Prediction Models for Silicon and Germanium Bond Dissociation Enthalpies
inferred← citeshttps://doi.org/10.1021/ci100050topenalex-snapshot
Extended-Connectivity Fingerprints
inferred← citeshttps://doi.org/10.1021/ci100297yopenalex-snapshot
MultiMCS: A Fast Algorithm for the Maximum Common Substructure Problem on Multiple Molecules
inferred← citeshttps://doi.org/10.1021/ci100473dopenalex-snapshot
Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors
inferred← citeshttps://doi.org/10.1021/ci200282zopenalex-snapshot
Power Keys: A Novel Class of Topological Descriptors Based on Exhaustive Subgraph Enumeration and their Application in Substructure Searching
inferred← citeshttps://doi.org/10.1021/ci6004178openalex-snapshot
TMACC: Interpretable Correlation Descriptors for Quantitative Structure−Activity Relationships
inferred← citeshttps://doi.org/10.1021/ci7000082openalex-snapshot
3D QSAR Methods: Phase and Catalyst Compared
inferred← citeshttps://doi.org/10.1021/ci7000633openalex-snapshot
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties
inferred← citeshttps://doi.org/10.1021/cn9000389openalex-snapshot
Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening
inferred← citeshttps://doi.org/10.1021/cr900238dopenalex-snapshot
Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction
inferred← citeshttps://doi.org/10.1021/jm300131xopenalex-snapshot
The Experimental Uncertainty of Heterogeneous Public Ki Data
inferred← citeshttps://doi.org/10.1021/jm4004285openalex-snapshot
QSAR Modeling: Where Have You Been? Where Are You Going To?
inferred← citeshttps://doi.org/10.1038/s41428-022-00734-9openalex-snapshot
Recent advances and challenges in experiment-oriented polymer informatics
inferred← citeshttps://doi.org/10.1038/s41540-026-00692-5openalex-snapshot
KinForm: kinetics-informed feature optimised representation models for enzyme kcat and KM prediction
inferred← citeshttps://doi.org/10.1038/s41598-018-27344-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-020-77327-0openalex-snapshot
Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models
inferred← citeshttps://doi.org/10.1038/s41598-021-99369-8openalex-snapshot
Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES
inferred← citeshttps://doi.org/10.1038/s41598-024-58122-7openalex-snapshot
Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors
inferred← citeshttps://doi.org/10.1038/s42003-025-09064-xopenalex-snapshot
MoleculeFormer is a GCN-transformer architecture for molecular property prediction
inferred← citeshttps://doi.org/10.1038/s42004-025-01434-0openalex-snapshot
An active representation learning method for reaction yield prediction with small-scale data
inferred← citeshttps://doi.org/10.1038/s42004-026-01938-3openalex-snapshot
A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs
inferred← citeshttps://doi.org/10.1038/s42256-021-00301-6openalex-snapshot
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations
inferred← citeshttps://doi.org/10.1039/9781847558879-00076openalex-snapshot
Pharmacophore-based Virtual Screening in Drug Discovery
inferred← citeshttps://doi.org/10.1039/b609116bopenalex-snapshot
Computer-aided drug design 2005–2007
inferred← citeshttps://doi.org/10.1039/c0md00165aopenalex-snapshot
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
inferred← citeshttps://doi.org/10.1039/c3cs60050eopenalex-snapshot
Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences
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/d0np00043dopenalex-snapshot
Machine learning approaches for elucidating the biological effects of natural products
inferred← citeshttps://doi.org/10.1039/d3cp00253eopenalex-snapshot
Accurate prediction of 11 B NMR chemical shift of BODIPYs via machine learning
inferred← citeshttps://doi.org/10.1039/d3dd00034fopenalex-snapshot
Molecular screening for solid–solid phase transitions by machine learning
inferred← citeshttps://doi.org/10.1039/d4dd00399copenalex-snapshot
DOPtools: a Python platform for descriptor calculation and model optimization
inferred← citeshttps://doi.org/10.1039/d4md00107aopenalex-snapshot
Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data
inferred← citeshttps://doi.org/10.1039/d5dd00437copenalex-snapshot
Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
inferred← citeshttps://doi.org/10.1039/d6cp03065copenalex-snapshot
Combining fingerprint-based deep neural networks with machine learning potentials for the study of eumelanin components
inferred← citeshttps://doi.org/10.1073/pnas.1810847116openalex-snapshot
Ligand biological activity predicted by cleaning positive and negative chemical correlations
inferred← citeshttps://doi.org/10.1080/10629360902949567openalex-snapshot
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
inferred← citeshttps://doi.org/10.1080/1062936x.2014.993702openalex-snapshot
Current situation on the availability of nanostructure–biological activity data
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1046189openalex-snapshot
QSAR study of some anti-hyperglycaemic sulphonylurea drugs
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/10.1080/1062936x.2020.1862297openalex-snapshot
SAR and QSAR research on tyrosinase inhibitors using machine learning methods
inferred← citeshttps://doi.org/10.1080/1062936x.2024.2371155openalex-snapshot
HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors
inferred← citeshttps://doi.org/10.1080/1062936x.2025.2535606openalex-snapshot
ToxAI_assistant: a web platform for a comprehensive study of the acute toxicity of xenobiotics following oral and intravenous administration in rats
inferred← citeshttps://doi.org/10.1080/10826070701191052openalex-snapshot
Structure‐Retention Correlation in Liquid Chromatography for Pharmaceutical Applications
inferred← citeshttps://doi.org/10.1080/17460441.2018.1496079openalex-snapshot
Challenges with multi-objective QSAR in drug discovery
inferred← citeshttps://doi.org/10.1080/17460441.2026.2661209openalex-snapshot
Leveraging machine learning for selective cannabinoid ligand discovery: methods, challenges, and opportunities
inferred← citeshttps://doi.org/10.1080/17568919.2024.2389773openalex-snapshot
Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking
inferred← citeshttps://doi.org/10.1093/bib/bbab317openalex-snapshot
Mol2Context-vec: learning molecular representation from context awareness for drug discovery
inferred← citeshttps://doi.org/10.1093/bib/bbw004openalex-snapshot
The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa566openalex-snapshot
Fast and accurate prediction of partial charges using Atom-Path-Descriptor-based machine learning
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae496openalex-snapshot
Attention-based approach to predict drug–target interactions across seven target superfamilies
inferred← citeshttps://doi.org/10.1093/bioinformatics/btn307openalex-snapshot
ChemmineR: a compound mining framework for R
inferred← citeshttps://doi.org/10.1101/2023.08.10.552794openalex-snapshot
Pharmacophoric-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities
inferred← citeshttps://doi.org/10.1101/2025.04.01.646719openalex-snapshot
Fleming: An AI Agent for Antibiotic Design for Mycobacterium tuberculosis
inferred← citeshttps://doi.org/10.1101/2025.06.16.659994openalex-snapshot
Count your bits: fingerprint benchmarking to assess broad chemical space representation
inferred← citeshttps://doi.org/10.1101/2025.07.14.664363openalex-snapshot
Improving ADMET prediction with descriptor augmentation of Mol2Vec embeddings
inferred← citeshttps://doi.org/10.1101/2025.11.02.686183openalex-snapshot
qcGEM: a graph-based molecular representation with quantum chemistry awareness
inferred← citeshttps://doi.org/10.1103/physrevlett.119.208101openalex-snapshot
Optimal Design of Experiments by Combining Coarse and Fine Measurements
inferred← citeshttps://doi.org/10.1109/icmla61862.2024.00089openalex-snapshot
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
inferred← citeshttps://doi.org/10.1111/cbdd.13657openalex-snapshot
Fingerprint‐based computational models of 5‐lipo‐oxygenase activating protein inhibitors: Activity prediction and structure clustering
inferred← citeshttps://doi.org/10.1140/epjqt/s40507-025-00385-8openalex-snapshot
Hybrid quantum neural networks with variational quantum regressor for enhancing QSPR modeling of CO2-capturing amine
inferred← citeshttps://doi.org/10.1142/s2737416526500365openalex-snapshot
Fragment-Based De Novo Discovery of Fourth-Generation EGFR-L858R/T790M/C797S-Tyrosine Kinase Inhibitors Targeting Catalytic Lys745 Residue to Overcome the Osimertinib Resistance
inferred← citeshttps://doi.org/10.1145/3674658.3674677openalex-snapshot
LSTM-GNN: A Multi-Channel Model for Molecular Properties Prediction
inferred← citeshttps://doi.org/10.1146/annurev-chembioeng-101220-102232openalex-snapshot
Data Science in Chemical Engineering: Applications to Molecular Science
inferred← citeshttps://doi.org/10.11606/t.60.2018.tde-11102018-082601openalex-snapshot
Triagem de inibidores da enzima DHODH de Leishmania major em Asteraceae: estudos metabolômicos e da relação estrutura-atividade quantitativa (QSAR)
inferred← citeshttps://doi.org/10.1186/1758-2946-1-22openalex-snapshot
Interpretable correlation descriptors for quantitative structure-activity relationships
inferred← citeshttps://doi.org/10.1186/1758-2946-5-26openalex-snapshot
Open-source platform to benchmark fingerprints for ligand-based virtual screening
inferred← citeshttps://doi.org/10.1186/s12859-024-05984-3openalex-snapshot
MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction
inferred← citeshttps://doi.org/10.1186/s13321-016-0148-0openalex-snapshot
Comparing structural fingerprints using a literature-based similarity benchmark
inferred← citeshttps://doi.org/10.1186/s13321-023-00693-0openalex-snapshot
Reconstruction of lossless molecular representations from fingerprints
inferred← citeshttps://doi.org/10.1186/s13321-024-00830-3openalex-snapshot
Effectiveness of molecular fingerprints for exploring the chemical space of natural products
inferred← citeshttps://doi.org/10.1186/s13321-025-00993-7openalex-snapshot
Predictive modeling of visible-light azo-photoswitches’ properties using structural features
inferred← citeshttps://doi.org/10.1186/s13321-025-01041-0openalex-snapshot
Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models
inferred← citeshttps://doi.org/10.1186/s13321-025-01043-yopenalex-snapshot
Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields
inferred← citeshttps://doi.org/10.1186/s13321-025-01045-wopenalex-snapshot
The topology of molecular representations and its influence on machine learning performance
inferred← citeshttps://doi.org/10.1186/s13321-025-01115-zopenalex-snapshot
How to build machine learning models able to extrapolate from standard to modified peptides
inferred← citeshttps://doi.org/10.1186/s13321-025-01137-7openalex-snapshot
CaliciBoost: Performance-driven evaluation of molecular representations for caco-2 permeability prediction
inferred← citeshttps://doi.org/10.1186/s13321-026-01245-yopenalex-snapshot
Count your bits: fingerprint benchmarking to assess broad chemical space representation
inferred← citeshttps://doi.org/10.12688/f1000research.22292.1openalex-snapshot
ccbmlib – a Python package for modeling Tanimoto similarity value distributions
inferred← citeshttps://doi.org/10.1557/s43579-024-00551-6openalex-snapshot
SMILES-based machine learning enables the prediction of corrosion inhibition capacity
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10325586/v1openalex-snapshot
Optimized Gradient Boosting Decision Tree Ensembles for Honeybee Toxicity Prediction Using the ApisTox Dataset
inferred← citeshttps://doi.org/10.2174/1386207322666190419111559openalex-snapshot
Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)
inferred← citeshttps://doi.org/10.2174/9789815049428123010005openalex-snapshot
Some Medicinal Chemistry Applications of the QSAR/QSPR Theory
inferred← citeshttps://doi.org/10.2316/journal.210.2010.1.210-1010openalex-snapshot
COMPARISON OF CHEMICAL DESCRIPTORS FOR PROTEIN–CHEMICAL INTERACTION PREDICTION
inferred← citeshttps://doi.org/10.24377/ljmu.t.00004554openalex-snapshot
Development of in silico models for the prediction of toxicity incorporating ADME information
inferred← citeshttps://doi.org/10.26434/chemrxiv-2021-czl9popenalex-snapshot
Ensemble learning-based quantitative structure-activity relationship platform predicts binding behavior of RNA-targeted small molecules
inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-bsmdlopenalex-snapshot
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-jzqkfopenalex-snapshot
Identification of Lysosomotropism using Explainable Machine Learning and Morphological Profiling Cell Painting Data
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-x6tjropenalex-snapshot
Advancing ML in ADME(T) prediction: A Focus on Hypothesis Testing and Best Practices
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-x6tjr-v3openalex-snapshot
Benchmarking ML in ADMET predictions: A focus on hypothesis testing practices
inferred← citeshttps://doi.org/10.26434/chemrxiv-2024-zr2vropenalex-snapshot
Molecular Fingerprints Optimization for Enhanced Predictive Modeling
inferred← citeshttps://doi.org/10.26434/chemrxiv.13202846openalex-snapshot
BiasNet: A model to predict ligand bias towards GPCR signaling
inferred← citeshttps://doi.org/10.26434/chemrxiv.6871628openalex-snapshot
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations
inferred← citeshttps://doi.org/10.32657/10356/155796openalex-snapshot
Perfluorooctane sulfonate and bisphenol a strongly interact with bacterial antigens and adversely modulate host immunity
inferred← citeshttps://doi.org/10.3389/fchem.2019.00773openalex-snapshot
Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial Hits
inferred← citeshttps://doi.org/10.3389/fendo.2022.986016openalex-snapshot
Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns
inferred← citeshttps://doi.org/10.3389/fphar.2020.00067openalex-snapshot
Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures
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