Search results for "QSAR"

Hits ?▲	Authors	Title	Venue	Year	Link	Author keywords
120	Guo-Zheng Li 0001, Hao-Hua Meng, Mary Qu Yang, Jack Y. Yang	Asymmetric Bagging and Feature Selection for Activities Prediction of Drug Molecules.	IMSCCS	2007	DBLP DOI BibTeX RDF	Feature Selection, Ensemble Learning, Bagging, QSAR, QSAR
91	A. Ammar Ghaibeh, Mikio Sasaki, Hiroshi Chuman	Using Voronoi Grid and SVM Linear Regression in Drug Discovery.	CIBCB	2006	DBLP DOI BibTeX RDF
88	Muhammad Muddassar, F. A. Pasha, Hwan Won Chung, Kyung Ho Yoo, Chang Hyun Oh, Seung Joo Cho	The Receptor Guided 3D-QSAR Method is a Powerful Tool to Design More Potent IGF-1R Inhibitors.	FBIT	2007	DBLP DOI BibTeX RDF
75	Irene Luque Ruiz, Miguel Ángel Gómez-Nieto	A Tool for the Calculation of Molecular Descriptors in the Development of QSAR Models.	ICCSA (1)	2008	DBLP DOI BibTeX RDF	Molecular descriptors, Java, QSAR, Computational Chemistry
73	Walter Cedeñto, Dimitris K. Agraflotis	Particle swarms for drug design.	Congress on Evolutionary Computation	2005	DBLP DOI BibTeX RDF
73	Walter Cedeño, Dimitris K. Agrafiotis	A Comparison of Particle Swarms Techniques for the Development of Quantitative Structure-Activity Relationship Models for Drug Design.	CSB Workshops	2005	DBLP DOI BibTeX RDF
70	Yen-Chih Chen, Jinn-Moon Yang, Chi-Hung Tsai, Cheng-Yan Kao	GEMPLS: A New QSAR Method Combining Generic Evolutionary Method and Partial Least Squares.	EvoWorkshops	2005	DBLP DOI BibTeX RDF
70	Zhiwei Wang, Gregory L. Durst, Russell C. Eberhart, Donald B. Boyd, Zina Ben-Miled	Particle Swarm Optimization and Neural Network Application for QSAR.	IPDPS	2004	DBLP DOI BibTeX RDF
57	Sivaprakasam Prasanna, Pankaj R. Daga, Aihua Xie, Robert J. Doerksen	Glycogen synthase kinase-3 inhibition by 3-anilino-4-phenylmaleimides: insights from 3D-QSAR and docking.	J. Comput. Aided Mol. Des.	2009	DBLP DOI BibTeX RDF	3D-QSAR, CoMFA, CoMSIA, GSK-3, Maleimides, Docking
57	Uko Maran, Sulev Sild	QSAR Modeling of Genotoxicity on Non-congeneric Sets of Organic Compounds.	Artif. Intell. Rev.	2003	DBLP DOI BibTeX RDF	Ames test, forward selection, molecular descriptors, mutagenicity, multi-linear regression, quantum chemical descriptors, neural network, QSAR
55	Emilio Benfenati	Modelling Aquatic Toxicity with Advanced Computational Techniques: Procedures to Standardize Data and Compare Models.	KELSI	2004	DBLP DOI BibTeX RDF
55	Marian Viorel Craciun, Daniel Neagu, Christoph König 0004, Severin Bumbaru	A Study of Aquatic Toxicity Using Artificial Neural Networks.	KES	2003	DBLP DOI BibTeX RDF
51	F. A. Pasha, Muhammad Muddassar, So Ha Lee, Taebo Sim, Jung-Mi Hah, Seung Joo Cho	In silico Ligand-Based (LB) and Docking-Based (DB) 3D-QSAR Study of Potent Chk2 Inhibitors.	FBIT	2007	DBLP DOI BibTeX RDF
51	Changjian Huang, Mark J. Embrechts, Nagamani Sukumar, Curt M. Breneman	Data Fusion and Auto-fusion for Quantitative Structure-Activity Relationship (QSAR).	ICANN (1)	2007	DBLP DOI BibTeX RDF
45	Shuxing Zhang, Alexander Golbraikh, Scott Oloff, Harold Kohn, Alexander Tropsha	A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models.	J. Chem. Inf. Model.	2006	DBLP DOI BibTeX RDF
42	Kirandeep Kaur, Tanaji T. Talele	Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1.	J. Comput. Aided Mol. Des.	2009	DBLP DOI BibTeX RDF	3D QSAR, CoMFA, CoMSIA, Indolinone, PDK1
39	Kazuya Nakao, Masaaki Fujikawa, Ryo Shimizu, Miki Akamatsu	QSAR application for the prediction of compound permeability with in silico descriptors in practical use.	J. Comput. Aided Mol. Des.	2009	DBLP DOI BibTeX RDF	PAMPA, Log P, pK a, PSA, Caco-2, QSAR
39	Alexander V. Gaiday, Igor A. Levandovskiy, Kendall G. Byler, Tatyana E. Shubina	Mechanism of Influenza A M2 Ion-Channel Inhibition: A Docking and QSAR Study.	ICCS (2)	2008	DBLP DOI BibTeX RDF	Influenza A, ion-channel inhibition, antiviral drugs, cage compounds, QSAR
36	Wei Li 0035, Yannan Zhao, Yixu Song, Zehong Yang	COX-2 activity prediction in Chinese medicine using neural network based ensemble learning methods.	IJCNN	2008	DBLP DOI BibTeX RDF
36	Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page	An integrated approach to feature invention and model construction for drug activity prediction.	ICML	2007	DBLP DOI BibTeX RDF
36	Vladimir Svetnik, Andy Liaw, Christopher Tong, Ting Wang	Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules.	Multiple Classifier Systems	2004	DBLP DOI BibTeX RDF
36	Ferenc Darvas, Ákos Papp, István Bágyi, Géza Ambrus, László Ürge	OpenMolGRID, a GRID Based System for Solving Large-Scale Drug Design Problems.	European Across Grids Conference	2004	DBLP DOI BibTeX RDF
33	Peng Zhou 0003, Xiang Chen, Zhicai Shang	Side-chain conformational space analysis (SCSA): A multi conformation-based QSAR approach for modeling and prediction of protein-peptide binding affinities.	J. Comput. Aided Mol. Des.	2009	DBLP DOI BibTeX RDF	Multi-conformation-based quantitative structure-activity relationship, Side-chain conformational space analysis, Rotamer library, Self-consistent mean field theory, Protein-peptide complex, HLA-A*0201-restricted CTL epitope
33	Riccardo Cardin, Lisa Michielan, Stefano Moro, Alessandro Sperduti	PCA-Based Representations of Graphs for Prediction in QSAR Studies.	ICANN (2)	2009	DBLP DOI BibTeX RDF	PCA for graphs, prediction on structured domains, supervised learning
33	Winston Yu-Chen Chen, Po-Yuan Chen, Calvin Yu-Chian Chen, Jing-Gung Chung	Exploring 3D-QSAR pharmacophore mapping of azaphenanthrenone derivatives for mPGES-1 inhibition Using HypoGen technique.	CIBCB	2008	DBLP DOI BibTeX RDF
33	Yubin Ji, Ran Tian, Wenhan Lin	QSAR and Molecular Docking Study of a Series of Combretastatin Analogues Tubulin Inhibitors.	LSMS (2)	2007	DBLP DOI BibTeX RDF
33	Marian Viorel Craciun, Adina Cocu, Luminita Dumitriu, Cristina Segal	A Hybrid Feature Selection Algorithm for the QSAR Problem.	International Conference on Computational Science (1)	2006	DBLP DOI BibTeX RDF
33	Alessio Micheli, Filippo Portera, Alessandro Sperduti	QSAR/QSPR Studies by Kernel Machines, Recursive Neural Networks and Their Integration.	WIRN	2003	DBLP DOI BibTeX RDF
30	Kamel Mansouri, José T. Moreira-Filho, Charles N. Lowe, Nathaniel Charest, Todd Martin, Valery Tkachenko, Richard S. Judson, Mike Conway, Nicole C. Kleinstreuer, Antony J. Williams	Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling.	J. Cheminformatics	2024	DBLP DOI BibTeX RDF
30	Joyce K. Daré, Matheus P. Freitas	Is conformation relevant for QSAR purposes? 2D Chemical representation in a 3D-QSAR perspective.	J. Comput. Chem.	2022	DBLP DOI BibTeX RDF
30	Zhenxing Wu, Minfeng Zhu, Yu Kang 0002, Elaine Lai-Han Leung, Tailong Lei, Chao Shen, Dejun Jiang 0002, Zhe Wang 0041, Dong-Sheng Cao 0001, Tingjun Hou	Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.	Briefings Bioinform.	2021	DBLP DOI BibTeX RDF
30	Shaoqi Chen, Dongyu Xue, Guohui Chuai, Qiang Yang 0001, Qi Liu 0019	FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery.	Bioinform.	2021	DBLP DOI BibTeX RDF
30	Amit Kumar Halder, M. Natália Dias Soeiro Cordeiro	QSAR-Co-X: an open source toolkit for multitarget QSAR modelling.	J. Cheminformatics	2021	DBLP DOI BibTeX RDF
30	Rino Ragno	www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices - the Py-CoMFA web application as tool to build models from pre-aligned datasets.	J. Comput. Aided Mol. Des.	2019	DBLP DOI BibTeX RDF
30	Pravin Ambure, Amit Kumar Halder, Humberto González Díaz, M. Natália Dias Soeiro Cordeiro	QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models.	J. Chem. Inf. Model.	2019	DBLP DOI BibTeX RDF
30	Nicolas Bloyet	Caractérisation et plongement de sous-graphes colorés: application à la construction de modèles structures à activité (QSAR). (Characterization and embeddings of colored subgraphs : application to quantitative structure-activity relationship (QSAR) models).		2019	RDF
30	João Paulo Ataide Martins, Marco Antônio Rougeth de Oliveira, Mário Sérgio Oliveira de Queiroz	Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.	J. Comput. Chem.	2018	DBLP DOI BibTeX RDF
30	Villu Ruusmann, Sulev Sild, Uko Maran	QSAR DataBank - an approach for the digital organization and archiving of QSAR model information.	J. Cheminformatics	2014	DBLP DOI BibTeX RDF
30	Richard Cox, Darren V. S. Green, Christopher N. Luscombe, Noj Malcolm, Stephen D. Pickett	QSAR workbench: automating QSAR modeling to drive compound design.	J. Comput. Aided Mol. Des.	2013	DBLP DOI BibTeX RDF
30	Sergii Novotarskyi	QSAR approaches to predict human cytochrome P450 inhibition (QSAR-basierter Ansatz zur Vorhersage der Enzymhemmung von humanem Cytochrom P450) (PDF / PS)		2013	RDF
30	Raghuvir R. S. Pissurlenkar, Vijay M. Khedkar, Radhakrishnan P. Iyer, Evans C. Coutinho	Ensemble QSAR: A QSAR method based on conformational ensembles and metric descriptors.	J. Comput. Chem.	2011	DBLP DOI BibTeX RDF
30	Eric J. Martin, Prasenjit Mukherjee, David C. Sullivan, Johanna M. Jansen	Profile-QSAR: A Novel meta-QSAR Method that Combines Activities across the Kinase Family To Accurately Predict Affinity, Selectivity, and Cellular Activity.	J. Chem. Inf. Model.	2011	DBLP DOI BibTeX RDF
30	Iurii Sushko	Applicability Domain of QSAR models (Anwendbarkeitsbereich von QSAR-Modellen) (PDF / PS)		2011	RDF
30	João Paulo Ataide Martins, Euzébio G. Barbosa, Kerly F. M. Pasqualoto, Márcia M. C. Ferreira	LQTA-QSAR: A New 4D-QSAR Methodology.	J. Chem. Inf. Model.	2009	DBLP DOI BibTeX RDF
30	Jui-Hua Hsieh, Xiang S. Wang, Denise G. Teotico, Alexander Golbraikh, Alexander Tropsha	Differentiation of AmpC beta-lactamase binders vs. decoys using classification k NN QSAR modeling and application of the QSAR classifier to virtual screening.	J. Comput. Aided Mol. Des.	2008	DBLP DOI BibTeX RDF
30	Sofie Van Damme, Patrick Bultinck	A new computer program for QSAR-analysis: ARTE-QSAR.	J. Comput. Chem.	2007	DBLP DOI BibTeX RDF
30	Andrzej Bak, Jaroslaw Polanski	Modeling Robust QSAR 3: SOM-4D-QSAR with Iterative Variable Elimination IVE-PLS: Application to Steroid, Azo Dye, and Benzoic Acid Series.	J. Chem. Inf. Model.	2007	DBLP DOI BibTeX RDF
30	Osvaldo Andrade Santos-Filho, Anton J. Hopfinger	Structure-Based QSAR Analysis of a Set of 4-Hydroxy-5, 6-dihydropyrones as Inhibitors of HIV-1 Protease: An Application of the Receptor-Dependent (RD) 4D-QSAR Formalism.	J. Chem. Inf. Model.	2006	DBLP DOI BibTeX RDF
30	Gyanendra Pandey, Anil K. Saxena	3D QSAR Studies on Protein Tyrosine Phosphatase 1B Inhibitors: Comparison of the Quality and Predictivity among 3D QSAR Models Obtained from Different Conformer-Based Alignments.	J. Chem. Inf. Model.	2006	DBLP DOI BibTeX RDF
30	Qi Shen, Jian-Hui Jiang, Jing-chao Tao, Guo-Li Shen, Ru-Qin Yu	Modified Ant Colony Optimization Algorithm for Variable Selection in QSAR Modeling: QSAR Studies of Cyclooxygenase Inhibitors.	J. Chem. Inf. Model.	2005	DBLP DOI BibTeX RDF
30	Rafal Gieleciak, Tomasz Magdziarz, Andrzej Bak, Jaroslaw Polanski	Modeling Robust QSAR. 1. Coding Molecules in 3D-QSAR - from a Point to Surface Sectors and Molecular Volumes.	J. Chem. Inf. Model.	2005	DBLP DOI BibTeX RDF
30	Craig L. Senese, Anton J. Hopfinger	A Simple Clustering Technique To Improve QSAR Model Selection and Predictivity: Application to a Receptor Independent 4D-QSAR Analysis of Cyclic Urea Derived Inhibitors of HIV-1 Protease.	J. Chem. Inf. Comput. Sci.	2003	DBLP DOI BibTeX RDF
30	Vellarkad N. Viswanadhan, Geoffrey A. Mueller, Subhash C. Basak, John N. Weinstein	Comparison of a Neural Net-Based QSAR Algorithm (PCANN) with Hologram- and Multiple Linear Regression-Based QSAR Approaches: Application to 1, 4-Dihydropyridine-Based Calcium Channel Antagonists.	J. Chem. Inf. Comput. Sci.	2001	DBLP DOI BibTeX RDF
30	Nikolai S. Zefirov, Vladimir A. Palyulin	QSAR for Boiling Points of "Small" Sulfides. Are the "High-Quality Structure-Property-Activity Regressions" the Real High Quality QSAR Models?	J. Chem. Inf. Comput. Sci.	2001	DBLP DOI BibTeX RDF
30	Kiyoshi Hasegawa, Toshiro Kimura, Kimito Funatsu	GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors.	J. Chem. Inf. Comput. Sci.	1999	DBLP DOI BibTeX RDF
30	Alexandru T. Balaban	Neural Networks in QSAR and Drug Design. Edited by J. Devillers. Volume 2 in the Series: Principles of QSAR and Drug Design. Academic Press: San Diego, 1996, 284 pp. ISBN 0-12-213815-5.	J. Chem. Inf. Comput. Sci.	1997	DBLP DOI BibTeX RDF
24	Nathan Brown	Chemoinformatics - an introduction for computer scientists.	ACM Comput. Surv.	2009	DBLP DOI BibTeX RDF	molecular modeling, drug discovery, docking, QSAR, Chemoinformatics, chemometrics
24	Ulrich Rückert 0002	Capacity Control for Partially Ordered Feature Sets.	ECML/PKDD (2)	2009	DBLP DOI BibTeX RDF	capacity control, partially ordered features, graph mining, QSAR
24	Kurt De Grave, Jan Ramon, Luc De Raedt	Active Learning for High Throughput Screening.	Discovery Science	2008	DBLP DOI BibTeX RDF	Chemical compounds, Optimization, Active Learning, QSAR
24	Axel J. Soto, Rocío L. Cecchini, Gustavo E. Vazquez, Ignacio Ponzoni	A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing.	EvoBIO	2008	DBLP DOI BibTeX RDF	hydrophobicity, Genetic Algorithms, Feature Selection, QSAR
24	Frank Lemke, Emilio Benfenati, Johann-Adolf Müller	Data-driven modeling and prediction of acute toxicity of pesticide residues.	SIGKDD Explor.	2006	DBLP DOI BibTeX RDF	DEMETRA, european chemicals policy, knowledge discovery workflow, pesticide toxicity, predictive QSAR models, self-organizing modeling, model validation
24	Uko Maran, Sulev Sild, Paolo Mazzatorta, Mosé Casalegno, Emilio Benfenati, Mathilde Romberg	Grid Computing for the Estimation of Toxicity: Acute Toxicity on Fathead Minnow (Pimephales promelas).	GCCB	2006	DBLP DOI BibTeX RDF	molecular descriptors, distributed computing, workflow, chemistry, QSAR, modelling and prediction
18	Gary B. Fogel, Jonathan Tran, Stephen Johnson, David Hecht	Machine learning approaches for customized docking scores: Modeling of inhibition of Mycobacterium tuberculosis enoyl acyl carrier protein reductase.	CIBCB	2010	DBLP DOI BibTeX RDF
18	Srinivas R. Alla, Akmal Aulia, Sunil Kumar, Rajni Garg	Using hybrid GA-ANN to predict biological activity of HIV protease inhibitors.	CIBCB	2008	DBLP DOI BibTeX RDF
18	Sulev Sild, Uko Maran, Mathilde Romberg, Bernd Schuller, Emilio Benfenati	OpenMolGRID: Using Automated Workflows in GRID Computing Environment.	EGC	2005	DBLP DOI BibTeX RDF
18	Saso Dzeroski, Ljupco Todorovski, Peter Ljubic	Inductive Databases of Polynomial Equations.	DaWaK	2004	DBLP DOI BibTeX RDF
18	Giuseppina C. Gini, Emilio Benfenati, Daniel Boley	Clustering and classification techniques to assess aquatic toxicity.	KES	2000	DBLP DOI BibTeX RDF
15	José Ramón Mora, Edgar Márquez, Noel Pérez-Pérez, Ernesto Contreras-Torres, Yunierkis Pérez-Castillo, Guillermín Agüero-Chapín, Felix Martinez-Rios, Yovani Marrero-Ponce, Stephen J. Barigye	Rethinking the applicability domain analysis in QSAR models.	J. Comput. Aided Mol. Des.	2024	DBLP DOI BibTeX RDF
15	Thomas M. Whitehead, Joel Strickland, Gareth John Conduit, Alexandre Borrel, Daniel Mucs, Irene Baskerville-Abraham	Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.	J. Chem. Inf. Model.	2024	DBLP DOI BibTeX RDF
15	Wouter Heyndrickx, Lewis H. Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, Peter Schmidtke, Nikolas Fechner, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balazs Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher N. Luscombe, Darren V. S. Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen D. Pickett, Gergely Ács, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans	MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.	J. Chem. Inf. Model.	2024	DBLP DOI BibTeX RDF
15	Muhammad Nauman Akram, Muhammad Amin, B. M. Golam Kibria, Mohammad Arashi, Adewale F. Lukman, Nimra Afzal	A new improved Liu estimator for the QSAR model with inverse Gaussian response.	Commun. Stat. Simul. Comput.	2024	DBLP DOI BibTeX RDF
15	Iffat Habib, Tahir Ali Chohan, Talha Ali Chohan, Fakhra Batool, Umair Khurshid, Anjum Khursheed, Ali Raza, Mukhtar Ansari, Arshad Hussain, Sirajudheen Anwar, Nasser A. Awadh Ali, Hammad Saleem	Integrated computational approaches for designing potent pyrimidine-based CDK9 inhibitors: 3D-QSAR, docking, and molecular dynamics simulations.	Comput. Biol. Chem.	2024	DBLP DOI BibTeX RDF
15	Supawit Phimonjit, Sutthiphon Thankam, Pawaris Techahongsa, Tipajin Thaipisutikul	Towards Drug Discovery: A Comparative Study of Machine Learning-enhanced QSAR Prediction.	KST	2024	DBLP DOI BibTeX RDF
15	Ronghe Zhou, Yong Zhang 0022, Kai He	A novel hybrid binary whale optimization algorithm with chameleon hunting mechanism for wrapper feature selection in QSAR classification model:A drug-induced liver injury case study.	Expert Syst. Appl.	2023	DBLP DOI BibTeX RDF
15	Gabriel Corrêa Veríssimo, Simone Queiroz Pantaleão, Philipe de Oliveira Fernandes, Jadson Castro Gertrudes, Thales Kronenberger, Káthia Maria Honório, Vinicius Gonçalves Maltarollo	MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.	J. Comput. Aided Mol. Des.	2023	DBLP DOI BibTeX RDF
15	Gyoung Jin Park, Nam Sook Kang	ADis-QSAR: a machine learning model based on biological activity differences of compounds.	J. Comput. Aided Mol. Des.	2023	DBLP DOI BibTeX RDF
15	Kohei Nemoto, Hiromasa Kaneko	De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models.	J. Chem. Inf. Model.	2023	DBLP DOI BibTeX RDF
15	Zixuan Cheng, Siaw San Hwang, Mrinal Bhave, Taufiq Rahman, Xavier Chee Wezen	Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ.	J. Chem. Inf. Model.	2023	DBLP DOI BibTeX RDF
15	Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris	Exploring QSAR Models for Activity-Cliff Prediction.	CoRR	2023	DBLP DOI BibTeX RDF
15	Zhifeng Gao, Xiaohong Ji, Guojiang Zhao, Hongshuai Wang, Hang Zheng, Guolin Ke, Linfeng Zhang	Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction.	CoRR	2023	DBLP DOI BibTeX RDF
15	Yuting Xu, Andy Liaw, Robert P. Sheridan, Vladimir Svetnik	Development and Evaluation of Conformal Prediction Methods for QSAR.	CoRR	2023	DBLP DOI BibTeX RDF
15	Damilola S. Bodun, Damilola A. Omoboyowa, Olaposi I. Omotuyi, Ezekiel A. Olugbogi, Toheeb A. Balogun, Chiamaka J. Ezeh, Emmanuel S. Omirin	QSAR-based virtual screening of traditional Chinese medicine for the identification of mitotic kinesin Eg5 inhibitors.	Comput. Biol. Chem.	2023	DBLP DOI BibTeX RDF
15	Hai Duc Nguyen, Min-Sun Kim	Identification of promising inhibitory heterocyclic compounds against acetylcholinesterase using QSAR, ADMET, biological activity, and molecular docking.	Comput. Biol. Chem.	2023	DBLP DOI BibTeX RDF
15	Clayton Fernando Rencilin, Joseph Christina Rosy, Krishnan Sundar	Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics.	Int. J. Comput. Biol. Drug Des.	2023	DBLP DOI BibTeX RDF
15	Piyush Kumar Yadav, Suchitra Singh, Ajay Kumar Singh	'3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity.	Comput. Biol. Medicine	2023	DBLP DOI BibTeX RDF
15	Goverdhan Lanka, Darakhshan Begum, Suvankar Banerjee, Nilanjan Adhikari, Yogeeswari P, Balaram Ghosh	Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors.	Comput. Biol. Medicine	2023	DBLP DOI BibTeX RDF
15	Rahul Singh, Parvin Kumar, Jayant Sindhu, Meena Devi, Ashwani Kumar, Sohan Lal, Devender Singh	Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies.	Comput. Biol. Medicine	2023	DBLP DOI BibTeX RDF
15	Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris	Exploring QSAR models for activity-cliff prediction.	J. Cheminformatics	2023	DBLP DOI BibTeX RDF
15	Taoufik Akabli, Hamid Toufik, Mourad Stitou, Fatima Lamchouri	Docking study and QSAR analysis based on the artificial neural network and multiple linear regression of novel harmine derivatives.	Int. J. Comput. Aided Eng. Technol.	2023	DBLP DOI BibTeX RDF
15	Antonina L. Nazarova, Aiichiro Nakano	VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling.	Mach. Learn. Knowl. Extr.	2022	DBLP DOI BibTeX RDF
15	Eleonora Proia, Alessio Ragno, Lorenzo Antonini, Manuela Sabatino, Milan Mladenovic, Roberto Capobianco, Rino Ragno	Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal.	J. Comput. Aided Mol. Des.	2022	DBLP DOI BibTeX RDF
15	Kiril Lanevskij, Remigijus Didziapetris, Andrius Sazonovas	Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.	J. Comput. Aided Mol. Des.	2022	DBLP DOI BibTeX RDF
15	Anita Rácz, Timothy B. Dunn, Dávid Bajusz, Taewon David Kim, Ramón Alain Miranda-Quintana, Károly Héberger	Extended continuous similarity indices: theory and application for QSAR descriptor selection.	J. Comput. Aided Mol. Des.	2022	DBLP DOI BibTeX RDF
15	William Bort, Daniyar Mazitov, Dragos Horvath, Fanny Bonachéra, Arkadii I. Lin, Gilles Marcou, Igor I. Baskin, Timur I. Madzhidov, Alexandre Varnek	Inverse QSAR: Reversing Descriptor-Driven Prediction Pipeline Using Attention-Based Conditional Variational Autoencoder.	J. Chem. Inf. Model.	2022	DBLP DOI BibTeX RDF
15	Rino Ragno, Anna Minarini, Eleonora Proia, Lorenzo Antonini, Andrea Milelli, Vincenzo Tumiatti, Marco Fiore, Pasquale Fino, Lavinia Rutigliano, Rossella Fioravanti, Tomoaki Tahara, Elena Pacella, Antonio Greco, Gianluca Canettieri, Maria Luisa Di Paolo, Enzo Agostinelli	Bovine Serum Amine Oxidase and Polyamine Analogues: Chemical Synthesis and Biological Evaluation Integrated with Molecular Docking and 3-D QSAR Studies.	J. Chem. Inf. Model.	2022	DBLP DOI BibTeX RDF
15	Thereza A. Soares, Ariane Nunes-Alves, Angelica Mazzolari, Fiorella Ruggiu, Guo-Wei Wei 0001, Kenneth M. Merz Jr.	The (Re)-Evolution of Quantitative Structure-Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods.	J. Chem. Inf. Model.	2022	DBLP DOI BibTeX RDF
15	María Virginia Sabando, Ignacio Ponzoni, Evangelos E. Milios, Axel J. Soto	Using molecular embeddings in QSAR modeling: does it make a difference?	Briefings Bioinform.	2022	DBLP DOI BibTeX RDF
15	Fahimeh Motamedi, Horacio Pérez Sánchez, Alireza Mehridehnavi, Afshin Fassihi, Fahimeh Ghasemi	Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies.	Bioinform.	2022	DBLP DOI BibTeX RDF
15	Xiaoying Song, Li Chai 0001, Jingxin Zhang 0001	Graph Signal Processing Approach to QSAR/QSPR Model Learning of Compounds.	IEEE Trans. Pattern Anal. Mach. Intell.	2022	DBLP DOI BibTeX RDF
15	Asmaa Raafat, Samar Mowafy, Sahar M. Abouseri, Marwa A. Fouad, Nahla A. Farag	Lead generation of cysteine based mesenchymal epithelial transition (c-Met) kinase inhibitors: Using structure-based scaffold hopping, 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics simulation.	Comput. Biol. Medicine	2022	DBLP DOI BibTeX RDF