On the public citation graph
Virtual Screening of Molecular Databases Using a Support Vector Machine
0 signed · 274 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
Drug design by machine learning: support vector machines for pharmaceutical data analysis
inferredcites →https://doi.org/10.1016/s1359-6446(02)02411-xopenalex-snapshot
Why do we need so many chemical similarity search methods?
inferredcites →https://doi.org/10.1021/ci00007a012openalex-snapshot
Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited
inferredcites →https://doi.org/10.1021/ci00007a013openalex-snapshot
Computer-aided molecular formula determination from mass, proton and carbon-13 NMR spectra
inferredcites →https://doi.org/10.1021/ci000397qopenalex-snapshot
Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination
inferredcites →https://doi.org/10.1021/ci010366aopenalex-snapshot
Is There a Difference between Leads and Drugs? A Historical Perspective
inferredcites →https://doi.org/10.1021/ci025586iopenalex-snapshot
Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs
inferredcites →https://doi.org/10.1021/ci025620topenalex-snapshot
Active Learning with Support Vector Machines in the Drug Discovery Process
inferredcites →https://doi.org/10.1021/ci0340916openalex-snapshot
Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions
inferredcites →https://doi.org/10.1021/ci034108kopenalex-snapshot
Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms
inferredcites →https://doi.org/10.1021/ci960466uopenalex-snapshot
Similarity Searching in Files of Three-Dimensional Chemical Structures: Evaluation of the EVA Descriptor and Combination of Rankings Using Data Fusion
inferredcites →https://doi.org/10.1021/ci9902696openalex-snapshot
JChem: Java Applets and Modules Supporting Chemical Database Handling from Web Browsers
inferredcites →https://doi.org/10.1021/cr00023a012openalex-snapshot
From atoms and bonds to three-dimensional atomic coordinates: automatic model builders
inferredcites →https://doi.org/10.1021/jm000375vopenalex-snapshot
Improved Scoring of Ligand−Protein Interactions Using OWFEG Free Energy Grids
inferredcites →https://doi.org/10.1021/jm030644sopenalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
inferredcites →https://doi.org/10.1021/jm970732aopenalex-snapshot
Three-Dimensional Quantitative Similarity−Activity Relationships (3D QSiAR) from SEAL Similarity Matrices
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1023/a:1020877402759openalex-snapshot
Current trends in lead discovery: Are we looking for the appropriate properties?
inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot
Rational selection of training and test sets for the development of validated QSAR models
inferredcites →https://doi.org/10.1038/nrd941openalex-snapshot
Integration of virtual and high-throughput screening
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1109/tnn.2004.828772openalex-snapshot
Feature Selection in MLPs and SVMs Based on Maximum Output Information
inferredcites →https://doi.org/10.1126/science.286.5439.531openalex-snapshot
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
inferredcites →https://doi.org/10.1126/science.286.5439.537openalex-snapshot
Sequencing Complex Polysaccharides
inferredcites →https://doi.org/10.1162/089976601300014493openalex-snapshot
Improvements to Platt's SMO Algorithm for SVM Classifier Design
inferredcites →https://doi.org/10.7551/mitpress/1130.003.0016openalex-snapshot
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9780470048672.wecb123openalex-snapshot
Small Molecule Inhibitors: Design and Selection
inferred← citeshttps://doi.org/10.1002/9780470116449.ch6openalex-snapshot
Applications of Support Vector Machines in Chemistry
inferred← citeshttps://doi.org/10.1002/9780470872352.ch9openalex-snapshot
Kernel Function Applications in Cheminformatics
inferred← citeshttps://doi.org/10.1002/9781118435762.ch3openalex-snapshot
Design and Selection of Small Molecule Inhibitors
inferred← citeshttps://doi.org/10.1002/9781119161110.ch7openalex-snapshot
Data Visualization and Analysis Using Kohonen Self‐Organizing Maps
inferred← citeshttps://doi.org/10.1002/9781394167258.ch6openalex-snapshot
Recent Advances in Drug Design With Machine Learning
inferred← citeshttps://doi.org/10.1002/9783527633326.ch11openalex-snapshot
Applied Virtual Screening: Strategies, Recommendations, and Caveats
inferred← citeshttps://doi.org/10.1002/9783527655984.ch02openalex-snapshot
Public Domain Databases for Medicinal Chemistry
inferred← citeshttps://doi.org/10.1002/agt2.365openalex-snapshot
Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers
inferred← citeshttps://doi.org/10.1002/aris.2009.1440430108openalex-snapshot
Similarity methods in chemoinformatics
inferred← citeshttps://doi.org/10.1002/cem.2916openalex-snapshot
Prediction of anti‐ HIV activity on the basis of stacked auto‐encoder
inferred← citeshttps://doi.org/10.1002/chem.202401838openalex-snapshot
Target Fisher: A Consensus Structure‐Based Target Prediction Tool, and its Application in the Discovery of Selective MAO‐B Inhibitors
inferred← citeshttps://doi.org/10.1002/chin.200532197openalex-snapshot
Virtual Screening of Molecular Databases Using a Support Vector Machine.
inferred← citeshttps://doi.org/10.1002/ima.22572openalex-snapshot
Thermal imaging method to evaluate childhood obesity based on machine learning techniques
inferred← citeshttps://doi.org/10.1002/jcc.21347openalex-snapshot
Identification of small molecule aggregators from large compound libraries by support vector machines
inferred← citeshttps://doi.org/10.1002/jcc.21411openalex-snapshot
In silico prediction and screening of γ‐secretase inhibitors by molecular descriptors and machine learning methods
inferred← citeshttps://doi.org/10.1002/jcc.23438openalex-snapshot
GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization
inferred← citeshttps://doi.org/10.1002/jcc.26831openalex-snapshot
Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network
inferred← citeshttps://doi.org/10.1002/jcc.27292openalex-snapshot
Machine learning‐assisted search for novel coagulants: When machine learning can be efficient even if data availability is low
inferred← citeshttps://doi.org/10.1002/jcla.20230openalex-snapshot
Detection and significance of serum protein markers of small‐cell lung cancer
inferred← citeshttps://doi.org/10.1002/jps.20985openalex-snapshot
Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
inferred← citeshttps://doi.org/10.1002/minf.200900014openalex-snapshot
Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach
inferred← citeshttps://doi.org/10.1002/minf.201800031openalex-snapshot
Development of Ligand‐based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries
inferred← citeshttps://doi.org/10.1002/pmic.200500833openalex-snapshot
Application of serum protein fingerprint in diagnosis of papillary thyroid carcinoma
inferred← citeshttps://doi.org/10.1002/prca.200780083openalex-snapshot
Analysis of urinary proteomic patterns for diabetic nephropathy by ProteinChip
inferred← citeshttps://doi.org/10.1002/prot.24541openalex-snapshot
Discerning intersecting fusion‐activation pathways in the Nipah virus using machine learning
inferred← citeshttps://doi.org/10.1002/qsar.200610084openalex-snapshot
Enhancing the Effectiveness of Ligand‐Based Virtual Screening Using Data Fusion
inferred← citeshttps://doi.org/10.1002/qsar.200960021openalex-snapshot
Prediction of HIV‐1 Protease Inhibitors Using Machine Learning Approaches
inferred← citeshttps://doi.org/10.1002/wcms.1640openalex-snapshot
Small molecule superposition: A comprehensive overview on pose scoring of the latest methods
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_135openalex-snapshot
Drug Design with Machine Learning
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_136openalex-snapshot
Drug Design, Molecular Descriptors in
inferred← citeshttps://doi.org/10.1007/978-1-4419-6045-0_19openalex-snapshot
Trends in Chemical Graph Data Mining
inferred← citeshttps://doi.org/10.1007/978-1-4419-7046-6_24openalex-snapshot
Prediction of Chemical-Protein Binding Activity Using Contrast Graph Patterns
inferred← citeshttps://doi.org/10.1007/978-1-60327-429-6_18openalex-snapshot
Optimization of the MAD Algorithm for Virtual Screening
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_21openalex-snapshot
Application of Support Vector Machine-Based Ranking Strategies to Search for Target-Selective Compounds
inferred← citeshttps://doi.org/10.1007/978-3-030-33582-3_25openalex-snapshot
Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1007/978-3-031-04998-9_10openalex-snapshot
Polypharmacology in Clinical Applications: Anti-infection Polypharmacology
inferred← citeshttps://doi.org/10.1007/978-3-031-04998-9_18openalex-snapshot
General Strategies for Rational Design and Discovery of Multitarget Drugs
inferred← citeshttps://doi.org/10.1007/978-3-319-16480-9_62openalex-snapshot
Support Vector Machine Prediction of Drug Solubility on GPUs
inferred← citeshttps://doi.org/10.1007/978-3-540-36266-1_10openalex-snapshot
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
inferred← citeshttps://doi.org/10.1007/978-3-642-27737-5_136-2openalex-snapshot
Drug Design, Molecular Descriptors in
inferred← citeshttps://doi.org/10.1007/978-981-97-2082-8_11openalex-snapshot
Prediction of Chronic Venous Insufficiency Condition Using Infrared Thermography and Machine Learning
inferred← citeshttps://doi.org/10.1007/s00256-019-03342-6openalex-snapshot
The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population
inferred← citeshttps://doi.org/10.1007/s00726-009-0329-5openalex-snapshot
Computational structure–activity study directs synthesis of novel antitumor enkephalin analogs
inferred← citeshttps://doi.org/10.1007/s10822-007-9112-4openalex-snapshot
Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening
inferred← citeshttps://doi.org/10.1007/s10822-012-9575-9openalex-snapshot
Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide
inferred← citeshttps://doi.org/10.1007/s10822-016-9960-xopenalex-snapshot
A D3R prospective evaluation of machine learning for protein-ligand scoring
inferred← citeshttps://doi.org/10.1007/s10822-018-0133-yopenalex-snapshot
Convolutional neural network scoring and minimization in the D3R 2017 community challenge
inferred← citeshttps://doi.org/10.1007/s11030-010-9232-yopenalex-snapshot
Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling
inferred← citeshttps://doi.org/10.1007/s11030-019-09961-4openalex-snapshot
Pharmacophore features for machine learning in pharmaceutical virtual screening
inferred← citeshttps://doi.org/10.1007/s11030-021-10326-zopenalex-snapshot
Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries
inferred← citeshttps://doi.org/10.1007/s11030-021-10329-wopenalex-snapshot
Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors
inferred← citeshttps://doi.org/10.1007/s11030-024-10997-4openalex-snapshot
Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1
inferred← citeshttps://doi.org/10.1007/s12032-008-9050-6openalex-snapshot
Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS
inferred← citeshttps://doi.org/10.1007/s13721-018-0174-0openalex-snapshot
Prediction of drug solubility on parallel computing architecture by support vector machines
inferred← citeshttps://doi.org/10.1007/springerreference_60308openalex-snapshot
Drug Design, Molecular Descriptors in
inferred← citeshttps://doi.org/10.1016/b978-0-12-820546-4.00003-9openalex-snapshot
Small molecule databases: A collection of promising bioactive molecules
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00014-5openalex-snapshot
Multiobjective optimization of biological and physical properties in drug discovery
inferred← citeshttps://doi.org/10.1016/j.aca.2006.08.031openalex-snapshot
Structure–activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines
inferred← citeshttps://doi.org/10.1016/j.asoc.2013.10.033openalex-snapshot
Improving drug discovery using hybrid softcomputing methods
inferred← citeshttps://doi.org/10.1016/j.bmc.2009.05.038openalex-snapshot
Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites
inferred← citeshttps://doi.org/10.1016/j.bmc.2012.03.030openalex-snapshot
Chemoinformatics: A view of the field and current trends in method development
inferred← citeshttps://doi.org/10.1016/j.bmc.2012.12.041openalex-snapshot
Retrospective group fusion similarity search based on eROCE evaluation metric
inferred← citeshttps://doi.org/10.1016/j.chemolab.2012.05.012openalex-snapshot
In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.cmpb.2014.08.009openalex-snapshot
Drug/nondrug classification using Support Vector Machines with various feature selection strategies
inferred← citeshttps://doi.org/10.1016/j.compag.2010.08.013openalex-snapshot
Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2021.107529openalex-snapshot
Ensembling machine learning models to boost molecular affinity prediction
inferred← citeshttps://doi.org/10.1016/j.curpro.2025.100015openalex-snapshot
Advances in machine learning for optimizing pharmaceutical drug discovery
inferred← citeshttps://doi.org/10.1016/j.diabres.2010.11.036openalex-snapshot
Analysis of urinary proteomic patterns for type 2 diabetic nephropathy by ProteinChip
inferred← citeshttps://doi.org/10.1016/j.drudis.2007.01.011openalex-snapshot
Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferred← citeshttps://doi.org/10.1016/j.ejmech.2006.12.028openalex-snapshot
In-silico drug screening method based on the protein–compound affinity matrix using the factor selection technique
inferred← citeshttps://doi.org/10.1016/j.ejmech.2011.01.020openalex-snapshot
Exploration of (S)-3-aminopyrrolidine as a potentially interesting scaffold for discovery of novel Abl and PI3K dual inhibitors
inferred← citeshttps://doi.org/10.1016/j.ejmech.2011.05.009openalex-snapshot
Could LogP be a principal determinant of biological activity in 18-crown-6 ethers? Synthesis of biologically active adamantane-substituted diaza-crowns
inferred← citeshttps://doi.org/10.1016/j.ejmech.2011.05.031openalex-snapshot
Combined SVM-based and docking-based virtual screening for retrieving novel inhibitors of c-Met
inferred← citeshttps://doi.org/10.1016/j.ejmech.2015.06.054openalex-snapshot
Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117287openalex-snapshot
Deep Convolutional Neural Network to improve the performances of screening process in LBVS
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2018.11.039openalex-snapshot
The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability
inferred← citeshttps://doi.org/10.1016/j.jcrysgro.2022.126641openalex-snapshot
Quantitative analysis of solid and liquid contents in reactive crystallization by in-situ Raman with support vector regression
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.03.003openalex-snapshot
Prediction of factor Xa inhibitors by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.12.002openalex-snapshot
A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2008.08.004openalex-snapshot
Data mining PubChem using a support vector machine with the Signature molecular descriptor: Classification of factor XIa inhibitors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2009.10.001openalex-snapshot
PubChem BioAssays as a data source for predictive models
inferred← citeshttps://doi.org/10.1016/j.jmgm.2011.06.006openalex-snapshot
Development of in silico models for pyrazoles and pyrimidine derivatives as cyclin-dependent kinase 2 inhibitors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2018.06.005openalex-snapshot
Visualizing convolutional neural network protein-ligand scoring
inferred← citeshttps://doi.org/10.1016/j.molcata.2009.12.008openalex-snapshot
Virtual screening prediction of new potential organocatalysts for direct aldol reactions
inferred← citeshttps://doi.org/10.1016/j.neo.2015.08.009openalex-snapshot
Identification of a Selective G1-Phase Benzimidazolone Inhibitor by a Senescence-Targeted Virtual Screen Using Artificial Neural Networks
inferred← citeshttps://doi.org/10.1016/j.procs.2011.04.219openalex-snapshot
GPU-accelerated Chemical Similarity Assessment for Large Scale Databases
inferred← citeshttps://doi.org/10.1016/j.rimni.2015.12.001openalex-snapshot
Predicción de solubilidad de fármacos usando máquinas de soporte vectorial sobre unidades de procesamiento gráfico
inferred← citeshttps://doi.org/10.1016/j.sbi.2018.01.006openalex-snapshot
Statistical and machine learning approaches to predicting protein–ligand interactions
inferred← citeshttps://doi.org/10.1016/j.thromres.2008.05.019openalex-snapshot
The potential biomarkers for thromboembolism detected by SELDI-TOF-MS
inferred← citeshttps://doi.org/10.1016/j.vibspec.2006.12.002openalex-snapshot
Studying aromatic compounds in infrared spectra based on support vector machine
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02008-1openalex-snapshot
Chapter 8 Machine Learning in Computational Chemistry
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02009-3openalex-snapshot
Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR
inferred← citeshttps://doi.org/10.1016/s1872-2067(18)63144-4openalex-snapshot
Advancements in biocatalysis: From computational to metabolic engineering
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01489openalex-snapshot
XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00646openalex-snapshot
A Multitask Approach to Learn Molecular Properties
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00054openalex-snapshot
Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00090openalex-snapshot
Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00737openalex-snapshot
Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00740openalex-snapshot
Protein–Ligand Scoring with Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00552openalex-snapshot
Differential Compound Prioritization via Bidirectional Selectivity Push with Power
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00584openalex-snapshot
Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b01145openalex-snapshot
libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.8b01404openalex-snapshot
Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b02147openalex-snapshot
Transfer Learning for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jpcc.0c00329openalex-snapshot
Deep Learning for Optoelectronic Properties of Organic Semiconductors
inferred← citeshttps://doi.org/10.1021/acschemneuro.1c00430openalex-snapshot
Identification of a Chlorogenic Ester as a Monoamine Oxidase (MAO-B) Inhibitor by Integrating “Traditional and Machine Learning” Virtual Screening and In Vitro as well as In Vivo Validation: A Lead against Neurodegenerative Disorders?
inferred← citeshttps://doi.org/10.1021/acsomega.7b01079openalex-snapshot
Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
inferred← citeshttps://doi.org/10.1021/ci050301yopenalex-snapshot
Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines
inferred← citeshttps://doi.org/10.1021/ci050348jopenalex-snapshot
New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching
inferred← citeshttps://doi.org/10.1021/ci050397wopenalex-snapshot
Virtual Screening Using Binary Kernel Discrimination: Analysis of Pesticide Data
inferred← citeshttps://doi.org/10.1021/ci050519kopenalex-snapshot
Assessing Different Classification Methods for Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci0505426openalex-snapshot
Virtual Screening Using Binary Kernel Discrimination: Effect of Noisy Training Data and the Optimization of Performance
inferred← citeshttps://doi.org/10.1021/ci100072zopenalex-snapshot
Pharmaceutical Perspectives of Nonlinear QSAR Strategies
inferred← citeshttps://doi.org/10.1021/ci100262sopenalex-snapshot
Discovery of Chemical Compound Groups with Common Structures by a Network Analysis Approach (Affinity Prediction Method)
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/ci100464bopenalex-snapshot
Discovery of Novel Pim-1 Kinase Inhibitors by a Hierarchical Multistage Virtual Screening Approach Based on SVM Model, Pharmacophore, and Molecular Docking
inferred← citeshttps://doi.org/10.1021/ci400090topenalex-snapshot
Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations
inferred← citeshttps://doi.org/10.1021/ci400120bopenalex-snapshot
SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes
inferred← citeshttps://doi.org/10.1021/ci4002712openalex-snapshot
Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci400331popenalex-snapshot
Predictions of BuChE Inhibitors Using Support Vector Machine and Naive Bayesian Classification Techniques in Drug Discovery
inferred← citeshttps://doi.org/10.1021/ci500147bopenalex-snapshot
Prediction of Compounds in Different Local Structure–Activity Relationship Environments Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci7002076openalex-snapshot
ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine
inferred← citeshttps://doi.org/10.1021/ci700333topenalex-snapshot
Bayesian Similarity Searching in High-Dimensional Descriptor Spaces Combined with Kullback-Leibler Descriptor Divergence Analysis
inferred← citeshttps://doi.org/10.1021/ci700369eopenalex-snapshot
Indirect Similarity Based Methods for Effective Scaffold-Hopping in Chemical Compounds
inferred← citeshttps://doi.org/10.1021/ci700461sopenalex-snapshot
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
inferred← citeshttps://doi.org/10.1021/ci800022eopenalex-snapshot
Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds
inferred← citeshttps://doi.org/10.1021/ci800079xopenalex-snapshot
Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics
inferred← citeshttps://doi.org/10.1021/ci800106eopenalex-snapshot
Design and Exploration of Target-Selective Chemical Space Representations
inferred← citeshttps://doi.org/10.1021/ci800329ropenalex-snapshot
Atomic Local Neighborhood Flexibility Incorporation into a Structured Similarity Measure for QSAR
inferred← citeshttps://doi.org/10.1021/ci800441copenalex-snapshot
Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors
inferred← citeshttps://doi.org/10.1021/ci900135uopenalex-snapshot
Virtual Screening of Abl Inhibitors from Large Compound Libraries by Support Vector Machines
inferred← citeshttps://doi.org/10.1021/ci900311jopenalex-snapshot
Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study
inferred← citeshttps://doi.org/10.1021/ci9003865openalex-snapshot
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
inferred← citeshttps://doi.org/10.1021/ci900419kopenalex-snapshot
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
inferred← citeshttps://doi.org/10.1021/ci900463uopenalex-snapshot
Binding Energy Landscape Analysis Helps to Discriminate True Hits from High-Scoring Decoys in Virtual Screening
inferred← citeshttps://doi.org/10.1021/ct300694eopenalex-snapshot
Quantifying Changes in Intrinsic Molecular Motion Using Support Vector Machines
inferred← citeshttps://doi.org/10.1021/jacs.4c11663openalex-snapshot
Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning
inferred← citeshttps://doi.org/10.1021/jm0603365openalex-snapshot
Comparison of Shape-Matching and Docking as Virtual Screening Tools
inferred← citeshttps://doi.org/10.1021/jm0608356openalex-snapshot
Benchmarking Sets for Molecular Docking
inferred← citeshttps://doi.org/10.1021/jm200153popenalex-snapshot
Thiazolides as Novel Antiviral Agents. 1. Inhibition of Hepatitis B Virus Replication
inferred← citeshttps://doi.org/10.1021/jm201264topenalex-snapshot
Thiazolides as Novel Antiviral Agents. 2. Inhibition of Hepatitis C Virus Replication
inferred← citeshttps://doi.org/10.1021/jm300501topenalex-snapshot
Public Domain Databases for Medicinal Chemistry
inferred← citeshttps://doi.org/10.1021/jm800504qopenalex-snapshot
In SilicoFunctional Profiling of Small Molecules and Its Applications
inferred← citeshttps://doi.org/10.1021/tx060230copenalex-snapshot
A Novel Approach Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) for Prediction of hERG Liability
inferred← citeshttps://doi.org/10.1038/hdy.2013.144openalex-snapshot
Genomic-enabled prediction with classification algorithms
inferred← citeshttps://doi.org/10.1038/s43246-022-00315-6openalex-snapshot
Graph neural networks for materials science and chemistry
inferred← citeshttps://doi.org/10.1039/9781847558879-00120openalex-snapshot
Molecular Similarity Analysis in Virtual Screening
inferred← citeshttps://doi.org/10.1039/9781847558879-00182openalex-snapshot
Probabilistic Approaches in Activity Prediction
inferred← citeshttps://doi.org/10.1039/c5ra10729fopenalex-snapshot
Multivariate statistical analysis methods in QSAR
inferred← citeshttps://doi.org/10.1039/d1cp05715dopenalex-snapshot
Understanding the interactions between lithium polysulfides and anchoring materials in advanced lithium–sulfur batteries using density functional theory
inferred← citeshttps://doi.org/10.1063/1.5022469openalex-snapshot
Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain
inferred← citeshttps://doi.org/10.1080/03009740701747152openalex-snapshot
Using SELDI‐TOF MS to identify serum biomarkers of rheumatoid arthritis
inferred← citeshttps://doi.org/10.1088/2632-2153/acb900openalex-snapshot
Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage
inferred← citeshttps://doi.org/10.1093/bib/bbp023openalex-snapshot
Recent advances in computer-aided drug design
inferred← citeshttps://doi.org/10.1093/bib/bbr028openalex-snapshot
Mining small-molecule screens to repurpose drugs
inferred← citeshttps://doi.org/10.1093/bib/bbz144openalex-snapshot
Deep learning of pharmacogenomics resources: moving towards precision oncology
inferred← citeshttps://doi.org/10.1093/nar/gkl999openalex-snapshot
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
inferred← citeshttps://doi.org/10.1093/nar/gkv1072openalex-snapshot
BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology
inferred← citeshttps://doi.org/10.1101/2025.11.12.688093openalex-snapshot
Off-target mapping enhances selectivity of machine learning-predicted CK2 inhibitors
inferred← citeshttps://doi.org/10.1101/605162openalex-snapshot
Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods
inferred← citeshttps://doi.org/10.1101/610444openalex-snapshot
Structural Learning of Proteins Using Graph Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/bibe.2008.4696654openalex-snapshot
GPM: A graph pattern matching kernel with diffusion for chemical compound classification
inferred← citeshttps://doi.org/10.1109/bibe.2008.4696655openalex-snapshot
Structure feature selection for chemical compound classification
inferred← citeshttps://doi.org/10.1109/bibm.2009.60openalex-snapshot
Neural Grammar Networks in QSAR Chemistry
inferred← citeshttps://doi.org/10.1109/bibm.2009.72openalex-snapshot
Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases
inferred← citeshttps://doi.org/10.1109/cibcb.2012.6217246openalex-snapshot
GPU-accelerated machine learning techniques enable QSAR modeling of large HTS data
inferred← citeshttps://doi.org/10.1109/cloudcom.2013.99openalex-snapshot
Using Iterative MapReduce for Parallel Virtual Screening
inferred← citeshttps://doi.org/10.1109/icpr.2018.8545246openalex-snapshot
Graph Memory Networks for Molecular Activity Prediction
inferred← citeshttps://doi.org/10.1109/tcbb.2009.80openalex-snapshot
GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2007.00605.xopenalex-snapshot
Atomic Property Fields: Generalized 3D Pharmacophoric Potential for Automated Ligand Superposition, Pharmacophore Elucidation and 3D QSAR
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2010.01059.xopenalex-snapshot
Potency‐Directed Similarity Searching Using Support Vector Machines
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2012.01415.xopenalex-snapshot
Discovery of Novel Bruton’s Tyrosine Kinase Inhibitors Using a Hybrid Protocol of Virtual Screening Approaches Based on SVM Model, Pharmacophore and Molecular Docking
inferred← citeshttps://doi.org/10.1111/jfbc.12955openalex-snapshot
A network pharmacology study on the triterpene saponins fromMedicago sativaL. for the treatment of Neurodegenerative diseases
inferred← citeshttps://doi.org/10.1137/1.9781611972818.72openalex-snapshot
The Infinite Push: A New Support Vector Ranking Algorithm that Directly Optimizes Accuracy at the Absolute Top of the List
inferred← citeshttps://doi.org/10.1142/9781860948732_0041openalex-snapshot
METHODS FOR EFFECTIVE VIRTUAL SCREENING AND SCAFFOLD-HOPPING IN CHEMICAL COMPOUNDS
inferred← citeshttps://doi.org/10.1145/1458082.1458212openalex-snapshot
Structure feature selection for graph classification
inferred← citeshttps://doi.org/10.1145/2629328openalex-snapshot
Structured Sparse Boosting for Graph Classification
inferred← citeshttps://doi.org/10.1145/2939672.2939832openalex-snapshot
Online Optimization Methods for the Quantification Problem
inferred← citeshttps://doi.org/10.1145/3447548.3469442openalex-snapshot
20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021)
inferred← citeshttps://doi.org/10.1159/000178883openalex-snapshot
Diagnosis of Pancreatic Adenocarcinoma Using Protein Chip Technology
inferred← citeshttps://doi.org/10.1162/neco_a_00972openalex-snapshot
Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve
inferred← citeshttps://doi.org/10.1186/1471-2105-10-s15-s10openalex-snapshot
Physicochemical property space distribution among human metabolites, drugs and toxins
inferred← citeshttps://doi.org/10.1186/1471-2105-11-s3-s8openalex-snapshot
Application of kernel functions for accurate similarity search in large chemical databases
inferred← citeshttps://doi.org/10.1186/1471-2105-12-s13-s25openalex-snapshot
In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance
inferred← citeshttps://doi.org/10.1186/1752-153x-1-29openalex-snapshot
On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes
inferred← citeshttps://doi.org/10.1186/1752-153x-2-3openalex-snapshot
A novel hybrid ultrafast shape descriptor method for use in virtual screening
inferred← citeshttps://doi.org/10.1186/1752-153x-6-139openalex-snapshot
Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries
inferred← citeshttps://doi.org/10.1186/1758-2946-4-s1-p12openalex-snapshot
Potency-directed similarity searching using support vector machines
inferred← citeshttps://doi.org/10.1186/s13321-014-0050-6openalex-snapshot
A ranking method for the concurrent learning of compounds with various activity profiles
inferred← citeshttps://doi.org/10.1287/ijoc.1100.0417openalex-snapshot
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
inferred← citeshttps://doi.org/10.13052/2245-1439.7111openalex-snapshot
A Novel Customized Big Data Analytics Framework for Drug Discovery
inferred← citeshttps://doi.org/10.13052/jcsm2245-1439.7111openalex-snapshot
A Novel Customized Big Data Analytics Framework for Drug Discovery
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1000405openalex-snapshot
FINDSITELHM: A Threading-Based Approach to Ligand Homology Modeling
showing the first 200 of 243 · page through all 243
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.