QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models

被引:75
作者
Ambure, Pravin [1 ]
Halder, Amit Kumar [1 ]
Gonzalez Diaz, Humbert [2 ]
Cordeiro, M. Natalia D. S. [1 ]
机构
[1] Univ Porto, Dept Chem & Biochem, LAQV REQUIMTE, P-4169007 Porto, Portugal
[2] Univ Basque Country UPV EHU, Dept Organ Chem 2, Leioa 48940, Spain
关键词
ARTIFICIAL NEURAL-NETWORKS; GENETIC ALGORITHM; ANTIBACTERIAL ACTIVITY; DISCOVERY; PREDICTION; POTENT; CHEMINFORMATICS; UNCERTAINTY; INHIBITORS; SELECTION;
D O I
10.1021/acs.jcim.9b00295
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Quantitative structure activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites. google.com/view/qsar-co) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.
引用
收藏
页码:2538 / 2544
页数:7
相关论文
共 57 条
[1]   Model for High-Throughput Screening of Multitarget Drugs in Chemical Neurosciences: Synthesis, Assay, and Theoretic Study of Rasagiline Carbamates [J].
Alonso, Nerea ;
Caamano, Olga ;
Romero-Duran, Francisco J. ;
Luan, Feng ;
Cordeiro, M. Natalia D. S. ;
Yanez, Matilde ;
Gonzalez-Diaz, Humberto ;
Garcia-Mera, Xerardo .
ACS CHEMICAL NEUROSCIENCE, 2013, 4 (10) :1393-1403
[2]  
Amata E, 2017, DATA BRIEF, V15, P281, DOI 10.1016/j.dib.2017.09.036
[3]   Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach [J].
Ambure, Pravin ;
Bhat, Jyotsna ;
Puzyn, Tomasz ;
Roy, Kunal .
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 2019, 37 (05) :1282-1306
[4]   Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against Aβ plaques in Alzheimer's disease: a predictive QSAR approach [J].
Ambure, Pravin ;
Roy, Kunal .
RSC ADVANCES, 2016, 6 (34) :28171-28186
[5]   "NanoBRIDGES" software: Open access tools to perform QSAR and nano-QSAR modeling [J].
Ambure, Pravin ;
Aher, Rahul Balasaheb ;
Gajewicz, Agnieszka ;
Puzyn, Tomasz ;
Roy, Kunal .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2015, 147 :1-13
[6]  
Aranda JF, 2017, SAR QSAR ENVIRON RES, V28, P749, DOI [10.1080/1062936X.2017.1377765, 10.1080/1062936x.2017.1377765]
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway [J].
Casanola-Martin, Gerardo M. ;
Huong Le-Thi-Thu ;
Perez-Gimenez, Facundo ;
Marrero-Ponce, Yovani ;
Merino-Sanjuan, Matilde ;
Abad, Concepcion ;
Gonzalez-Diaz, Humberto .
MOLECULAR DIVERSITY, 2015, 19 (02) :347-356
[9]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[10]   QSAR Modeling: Where Have You Been? Where Are You Going To? [J].
Cherkasov, Artem ;
Muratov, Eugene N. ;
Fourches, Denis ;
Varnek, Alexandre ;
Baskin, Igor I. ;
Cronin, Mark ;
Dearden, John ;
Gramatica, Paola ;
Martin, Yvonne C. ;
Todeschini, Roberto ;
Consonni, Viviana ;
Kuz'min, Victor E. ;
Cramer, Richard ;
Benigni, Romualdo ;
Yang, Chihae ;
Rathman, James ;
Terfloth, Lothar ;
Gasteiger, Johann ;
Richard, Ann ;
Tropsha, Alexander .
JOURNAL OF MEDICINAL CHEMISTRY, 2014, 57 (12) :4977-5010