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Baltasar-Marchueta M, Llona L, M-Alicante S, Barbolla I, Ibarluzea MG, Ramis R, Salomon AM, Fundora B, Araujo A, Muguruza-Montero A, Nuñez E, Pérez-Olea S, Villanueva C, Leonardo A, Arrasate S, Sotomayor N, Villarroel A, Bergara A, Lete E, González-Díaz H. Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy. Biomed Pharmacother 2024; 174:116602. [PMID: 38636396 DOI: 10.1016/j.biopha.2024.116602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca2+-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.
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Affiliation(s)
- Maider Baltasar-Marchueta
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Leire Llona
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | | | - Iratxe Barbolla
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Markel Garcia Ibarluzea
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Rafael Ramis
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Ane Miren Salomon
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Brenda Fundora
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Ariane Araujo
- Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain
| | | | - Eider Nuñez
- Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain
| | - Scarlett Pérez-Olea
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Christian Villanueva
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Aritz Leonardo
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain
| | | | - Aitor Bergara
- Donostia International Physics Center, Donostia, Spain; Departament of Physics, University of the Basque Country, UPV/EHU, Leioa, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, Leioa 48940, Spain; Biofisika Institute, CSIC-UPV/EHU, Leioa 48940, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48011, Spain.
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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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Pandey V. Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach. Chem Biodivers 2024; 21:e202301213. [PMID: 38109053 DOI: 10.1002/cbdv.202301213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023]
Abstract
Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.
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Affiliation(s)
- Vandana Pandey
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
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Begum S, Shareef MZ, Bharathi K. Part-II- in silico drug design: application and success. PHYSICAL SCIENCES REVIEWS 2021. [DOI: 10.1515/psr-2018-0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.
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Affiliation(s)
- Shaheen Begum
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
| | - Mohammad Zubair Shareef
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
| | - Koganti Bharathi
- Institute of Pharmaceutical Technology , Sri Padmavati Mahila Visvavidyalayam , 517501 Tirupati , Andhra Pradesh , India
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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Kasteel EEJ, Westerink RHS. Refining in vitro and in silico neurotoxicity approaches by accounting for interspecies and interindividual differences in toxicodynamics. Expert Opin Drug Metab Toxicol 2021; 17:1007-1017. [PMID: 33586568 DOI: 10.1080/17425255.2021.1885647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
INTRODUCTION The process of chemical risk assessment traditionally relies on animal experiments and associated default uncertainty factors to account for interspecies and interindividual differences. To work toward a more precise and personalized risk assessment, these uncertainty factors should be refined and replaced by chemical-specific adjustment factors (CSAFs). AREAS COVERED This concise review discusses alternative (in vitro/in silico) approaches that can be used to assess interspecies and interindividual differences in toxicodynamics, ranging from targeted to more integrated approaches. Although data are available on interspecies differences, the increasing use of human-induced pluripotent stem cell (hiPSC)-derived neurons may provide opportunities to also assess interindividual variability in neurotoxicity. More integrated approaches, like adverse outcome pathways (AOPs) can provide a more quantitative understanding of the toxicodynamics of a chemical. EXPERT OPINION To improve chemical risk assessment, refinement of uncertainty factors is crucial. In vitro and in silico models can facilitate the development of CSAFs, but still these models cannot always capture the complexity of the in vivo situation, thereby potentially hampering regulatory acceptance. The combined use of more integrated approaches, like AOPs and physiologically based kinetic models, can aid in structuring data and increasing suitability of alternative approaches for regulatory purposes.
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Affiliation(s)
- Emma E J Kasteel
- Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Remco H S Westerink
- Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
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Keyvanpour MR, Shirzad MB. An Analysis of QSAR Research Based on Machine Learning Concepts. Curr Drug Discov Technol 2020; 18:17-30. [PMID: 32178612 DOI: 10.2174/1570163817666200316104404] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/22/2019] [Accepted: 10/28/2019] [Indexed: 11/22/2022]
Abstract
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.
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Affiliation(s)
| | - Mehrnoush Barani Shirzad
- Data Mining Research Laboratory, Department of Computer Engineering, Alzahra University, Tehran, Iran
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Wei Y, Li W, Du T, Hong Z, Lin J. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method. Int J Mol Sci 2019; 20:ijms20143572. [PMID: 31336592 PMCID: PMC6678913 DOI: 10.3390/ijms20143572] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 07/13/2019] [Accepted: 07/21/2019] [Indexed: 12/11/2022] Open
Abstract
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.
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Affiliation(s)
- Yu Wei
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Wei Li
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China
| | - Tengfei Du
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Zhangyong Hong
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China.
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China.
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
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In silico evaluation of 5-hydroxypyrazoles as LSD1 inhibitors based on molecular docking derived descriptors. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2018.11.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Khan PM, Roy K. Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR). Expert Opin Drug Discov 2018; 13:1075-1089. [PMID: 30372648 DOI: 10.1080/17460441.2018.1542428] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation. Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers. Expert opinion: Novel sets of descriptors are being continuously introduced to this field; therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.
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Affiliation(s)
- Pathan Mohsin Khan
- a Department of Pharmacoinformatics , National Institute of Pharmaceutical Educational and Research (NIPER) , Kolkata , India
| | - Kunal Roy
- b Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
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