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Sharallah OA, Poddar NK, Alwadan OA. Delineation of the role of G6PD in Alzheimer's disease and potential enhancement through microfluidic and nanoparticle approaches. Ageing Res Rev 2024; 99:102394. [PMID: 38950868 DOI: 10.1016/j.arr.2024.102394] [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: 03/07/2024] [Revised: 06/16/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative pathologic entity characterized by the abnormal presence of tau and macromolecular Aβ deposition that leads to the degeneration or death of neurons. In addition to that, glucose-6-phosphate dehydrogenase (G6PD) has a multifaceted role in the process of AD development, where it can be used as both a marker and a target. G6PD activity is dysregulated due to its contribution to oxidative stress, neuroinflammation, and neuronal death. In this context, the current review presents a vivid depiction of recent findings on the relationship between AD progression and changes in the expression or activity of G6PD. The efficacy of the proposed G6PD-based therapeutics has been demonstrated in multiple studies using AD mouse models as representative animal model systems for cognitive decline and neurodegeneration associated with this disease. Innovative therapeutic insights are made for the boosting of G6PD activity via novel innovative nanotechnology and microfluidics tools in drug administration technology. Such approaches provide innovative methods of surpassing the blood-brain barrier, targeting step-by-step specific neural pathways, and overcoming biochemical disturbances that accompany AD. Using different nanoparticles loaded with G6DP to target specific organs, e.g., G6DP-loaded liposomes, enhances BBB penetration and brain distribution of G6DP. Many nanoparticles, which are used for different purposes, are briefly discussed in the paper. Such methods to mimic BBB on organs on-chip offer precise disease modeling and drug testing using microfluidic chips, requiring lower sample amounts and producing faster findings compared to conventional techniques. There are other contributions to microfluid in AD that are discussed briefly. However, there are some limitations accompanying microfluidics that need to be worked on to be used for AD. This study aims to bridge the gap in understanding AD with the synergistic use of promising technologies; microfluid and nanotechnology for future advancements.
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Affiliation(s)
- Omnya A Sharallah
- PharmD Program, Egypt-Japan University of Science and Technology (EJUST), New Borg El Arab, Alexandria 21934, Egypt
| | - Nitesh Kumar Poddar
- Department of Biosciences, Manipal University Jaipur, Dehmi Kalan, Jaipur-Ajmer Expressway, Jaipur, Rajasthan 303007, India.
| | - Omnia A Alwadan
- PharmD Program, Egypt-Japan University of Science and Technology (EJUST), New Borg El Arab, Alexandria 21934, Egypt
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2
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Yamasan BE, Korkmaz S. Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays. Balkan Med J 2024; 41:186-192. [PMID: 38462979 PMCID: PMC11077922 DOI: 10.4274/balkanmedj.galenos.2024.2024-1-73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/15/2024] [Indexed: 03/12/2024] Open
Abstract
Background The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. Aims To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. Methods We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). Results The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. Conclusion This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.
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Affiliation(s)
- Bilge Eren Yamasan
- Department of Biophysics, Trakya University Faculty of Medicine, Edirne, Türkiye
| | - Selçuk Korkmaz
- Department of Biostatistics and Medical Informatics, Trakya University Faculty of Medicine, Edirne, Türkiye
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3
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Ali H, Samad A, Ajmal A, Ali A, Ali I, Danial M, Kamal M, Ullah M, Ullah R, Kalim M. Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches. Pharmaceuticals (Basel) 2023; 16:1124. [PMID: 37631039 PMCID: PMC10459760 DOI: 10.3390/ph16081124] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/20/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen's survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub.
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Affiliation(s)
- Hamid Ali
- Department of Biosciences, COMSATS University Islamabad, Park Road, Tarlai Kalan, Islamabad 44000, Pakistan
| | - Abdus Samad
- Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan; (A.S.); (A.A.); (M.D.); (M.K.)
| | - Amar Ajmal
- Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan; (A.S.); (A.A.); (M.D.); (M.K.)
| | - Amjad Ali
- Faculty of Biological Sciences, Department of Biochemistry, Quaid-i-Azam University, Islamabad 45320, Pakistan;
| | - Ijaz Ali
- Centre for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, Hawally 32093, Kuwait;
| | - Muhammad Danial
- Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan; (A.S.); (A.A.); (M.D.); (M.K.)
| | - Masroor Kamal
- Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan; (A.S.); (A.A.); (M.D.); (M.K.)
| | - Midrar Ullah
- Department of Biotechnology, Shaheed Benazir Bhutto University Sheringal, Dir Upper 18050, Pakistan;
| | - Riaz Ullah
- Department of Pharmacognosy, College of Pharmacy King Saud University, Riyadh 11451, Saudi Arabia;
| | - Muhammad Kalim
- Department of Microbiology and Immunology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA;
- Houston Methodist Cancer Center/Weill Cornel Medicine, Houston, TX 77030, USA
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4
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Win ZM, Cheong AMY, Hopkins WS. Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time. J Chem Inf Model 2023; 63:1906-1913. [PMID: 36926888 DOI: 10.1021/acs.jcim.2c01373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.
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Affiliation(s)
- Zaw-Myo Win
- Centre for Eye and Vision Research, Hong Kong Science Park, New Territories 999077, Hong Kong.,School of Optometry, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong.,Department of Chemistry, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
| | - Allen M Y Cheong
- Centre for Eye and Vision Research, Hong Kong Science Park, New Territories 999077, Hong Kong.,School of Optometry, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong
| | - W Scott Hopkins
- Centre for Eye and Vision Research, Hong Kong Science Park, New Territories 999077, Hong Kong.,Department of Chemistry, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.,Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.,WaterMine Innovation, Inc., Waterloo, Ontario N0B 2T0, Canada
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5
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Choudhary R, Walhekar V, Muthal A, Kumar D, Bagul C, Kulkarni R. Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR. J Biomol Struct Dyn 2023; 41:12445-12463. [PMID: 36762704 DOI: 10.1080/07391102.2023.2175263] [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: 11/10/2022] [Accepted: 01/03/2023] [Indexed: 02/11/2023]
Abstract
This research manuscript aims to find the most effective epidermal growth factor receptor (EGFR) inhibitors from millions of in house compounds through Machine Learning (ML) techniques. ML-based structure activity relationship (SAR) models were validated to predict biological activity of untested novel molecules. Six ML algorithms, including k nearest neighbour (KNN), decision tree (DT), Logistic Regression, support vector machine (SVM), multilinear regression (MLR), and random forest (RF), were used to build for activity prediction. Among these, RF classifier (accuracy for train and test set is 90% and 81%) and RF regressor (R2 and MSE for trainset is 0.83 and 0.29 and for test set, 0.69 and 0.46) showed good predictive performance. Also, the six most essential features that affect the biological activity parameter and highly contribute to model development were successfully selected by the variable importance technique. RF regression model was used to predict the biological activity expressed as pIC50 of nearly ten million molecules while RF classification model classifies those molecules into active, moderately active, and least active according to their predicted pIC50. Based on two models, thousand molecules from million molecules with higher predicted pIC50 values and classified as active were selected for molecular docking. Based on the docking scores, predicted pIC50, and binding interactions with MET769 residue, compounds, i.e., Zinc257233137, Zinc257232249, and Zinc101379788, were identified as potential EGFR inhibitors with predicted pIC50 7.72, 7.85, and 7.70. Dynamics studies were also performed on Zinc257233137 to illustrate that it has good binding free energy and stable hydrogen bonding interactions with EGFR. These molecules can be used for further research and proved to be the novel drugs for EGFR in cancer treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rekha Choudhary
- Department of Pharmaceutical Chemistry, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
| | - Vinayak Walhekar
- Department of Pharmaceutical Chemistry, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
| | - Amol Muthal
- Department of Pharmacology, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
| | - Dilip Kumar
- Department of Pharmaceutical Chemistry, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
- Department of Entomology, University of California, Davis, Davis, California, USA
- UC Davis Comprehensive Cancer Centre, University of California, Davis, Davis, California, USA
| | - Chandrakant Bagul
- Department of Pharmaceutical Chemistry, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
| | - Ravindra Kulkarni
- Department of Pharmaceutical Chemistry, BVDU'S Poona College of Pharmacy, Pune, Maharashtra, India
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6
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Trisciuzzi D, Siragusa L, Baroni M, Cruciani G, Nicolotti O. An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. J Chem Inf Model 2022; 62:6812-6824. [PMID: 36320100 DOI: 10.1021/acs.jcim.2c00583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123Perugia (PG), Italy
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy
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7
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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8
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Narayanan H, Dingfelder F, Butté A, Lorenzen N, Sokolov M, Arosio P. Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation. Trends Pharmacol Sci 2021; 42:151-165. [DOI: 10.1016/j.tips.2020.12.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/10/2020] [Accepted: 12/16/2020] [Indexed: 12/19/2022]
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9
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Gally JM, Bourg S, Fogha J, Do QT, Aci-Sèche S, Bonnet P. VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening. Curr Med Chem 2021; 27:6480-6494. [PMID: 31242833 DOI: 10.2174/0929867326666190614160451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/11/2019] [Accepted: 05/24/2019] [Indexed: 01/21/2023]
Abstract
Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.
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Affiliation(s)
- José-Manuel Gally
- Institut de Chimie Organique et Analytique (ICOA), Universite d'Orleans, UMR CNRS 7311, BP 6759, 45067 Orleans, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), Universite d'Orleans, UMR CNRS 7311, BP 6759, 45067 Orleans, France
| | - Jade Fogha
- Institut de Chimie Organique et Analytique (ICOA), Universite d'Orleans, UMR CNRS 7311, BP 6759, 45067 Orleans, France
| | - Quoc-Tuan Do
- Greenpharma S.A.S. 3, allee du Titane, 45100 Orleans, France
| | - Samia Aci-Sèche
- Institut de Chimie Organique et Analytique (ICOA), Universite d'Orleans, UMR CNRS 7311, BP 6759, 45067 Orleans, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), Universite d'Orleans, UMR CNRS 7311, BP 6759, 45067 Orleans, France
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10
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Carpenter K, Pilozzi A, Huang X. A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction. Molecules 2020; 25:E3372. [PMID: 32722290 PMCID: PMC7435591 DOI: 10.3390/molecules25153372] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/20/2020] [Accepted: 07/24/2020] [Indexed: 01/01/2023] Open
Abstract
The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC50 of the target compounds. The performance of the models was assessed primarily through analysis of the Q2 values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.
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Affiliation(s)
| | | | - Xudong Huang
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; (K.C.); (A.P.)
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11
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Korkmaz S. Deep Learning-Based Imbalanced Data Classification for Drug Discovery. J Chem Inf Model 2020; 60:4180-4190. [DOI: 10.1021/acs.jcim.9b01162] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Selçuk Korkmaz
- Trakya University Faculty of Medicine, Department of Biostatistics and Medical Informatics, Edirne, Turkey
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12
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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13
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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Saddala MS, Lennikov A, Huang H. Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches. Int J Mol Sci 2020; 21:ijms21041523. [PMID: 32102234 PMCID: PMC7073180 DOI: 10.3390/ijms21041523] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 02/18/2020] [Accepted: 02/21/2020] [Indexed: 02/06/2023] Open
Abstract
Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack of Nicotine Adenosine Dinucleotide Phosphate (NADPH) and produces stress conditions that can cause oxidative injury to photoreceptors, retinal cells, and blood barrier function. In this study, we constructed pharmacophore-based models based on the complex of G6PD with compound AG1 (G6PD activator) followed by virtual screening. Fifty-three hit molecules were mapped with core pharmacophore features. We performed molecular descriptor calculation, clustering, and principal component analysis (PCA) to pharmacophore hit molecules and further applied statistical machine learning methods. Optimal performance of pharmacophore modeling and machine learning approaches classified the 53 hits as drug-like (18) and nondrug-like (35) compounds. The drug-like compounds further evaluated our established cheminformatics pipeline (molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis). Finally, five lead molecules with different scaffolds were selected by binding energies and in silico ADMET properties. This study proposes that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability to find potential G6PD activators used for G6PD deficiency diseases. Moreover, these compounds can be considered as safe agents for further validation studies at the cell level, animal model, and even clinic setting.
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15
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Pirzada RH, Javaid N, Choi S. The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions. Genes (Basel) 2020; 11:E131. [PMID: 32012695 PMCID: PMC7074480 DOI: 10.3390/genes11020131] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/22/2020] [Accepted: 01/22/2020] [Indexed: 02/07/2023] Open
Abstract
Inflammasomes are intracellular multiprotein complexes in the cytoplasm that regulate inflammation activation in the innate immune system in response to pathogens and to host self-derived molecules. Recent advances greatly improved our understanding of the activation of nucleotide-binding oligomerization domain-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasomes at the molecular level. The NLRP3 belongs to the subfamily of NLRP which activates caspase 1, thus causing the production of proinflammatory cytokines (interleukin 1β and interleukin 18) and pyroptosis. This inflammasome is involved in multiple neurodegenerative and metabolic disorders including Alzheimer's disease, multiple sclerosis, type 2 diabetes mellitus, and gout. Therefore, therapeutic targeting to the NLRP3 inflammasome complex is a promising way to treat these diseases. Recent research advances paved the way toward drug research and development using a variety of machine learning-based and artificial intelligence-based approaches. These state-of-the-art approaches will lead to the discovery of better drugs after the training of such a system.
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Affiliation(s)
| | | | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea; (R.H.P.); (N.J.)
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16
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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Application of Negative Design To Design a More Desirable Virtual Screening Library. J Med Chem 2020; 63:4411-4429. [DOI: 10.1021/acs.jmedchem.9b01476] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Jun-Hong He
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
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17
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 223] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key Topics in Molecular Docking for Drug Design. Int J Mol Sci 2019; 20:E4574. [PMID: 31540192 PMCID: PMC6769580 DOI: 10.3390/ijms20184574] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 12/18/2022] Open
Abstract
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
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Affiliation(s)
- Pedro H M Torres
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.
| | - Ana C R Sodero
- Department of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Paula Jofily
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Floriano P Silva-Jr
- Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil.
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Stephenson N, Shane E, Chase J, Rowland J, Ries D, Justice N, Zhang J, Chan L, Cao R. Survey of Machine Learning Techniques in Drug Discovery. Curr Drug Metab 2019; 20:185-193. [DOI: 10.2174/1389200219666180820112457] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 01/01/2018] [Accepted: 03/19/2018] [Indexed: 12/19/2022]
Abstract
Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.
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Affiliation(s)
- Natalie Stephenson
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Emily Shane
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jessica Chase
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jason Rowland
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - David Ries
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Nicola Justice
- Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jie Zhang
- Key Laboratory of Hebei Province for Plant Physiology and Molecular Pathology, College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Leong Chan
- School of Business, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
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20
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Carpenter KA, Cohen DS, Jarrell JT, Huang X. Deep learning and virtual drug screening. Future Med Chem 2018; 10:2557-2567. [PMID: 30288997 PMCID: PMC6563286 DOI: 10.4155/fmc-2018-0314] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 09/21/2018] [Indexed: 12/21/2022] Open
Abstract
Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.
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Affiliation(s)
- Kristy A Carpenter
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - David S Cohen
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Juliet T Jarrell
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Xudong Huang
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
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21
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Carpenter KA, Huang X. Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review. Curr Pharm Des 2018; 24:3347-3358. [PMID: 29879881 PMCID: PMC6327115 DOI: 10.2174/1381612824666180607124038] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 01/11/2023]
Abstract
BACKGROUND Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. OBJECTIVE The study aims to review ML-based methods used for VS and applications to Alzheimer's Disease (AD) drug discovery. METHODS To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). RESULTS All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. CONCLUSION Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development.
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Affiliation(s)
- Kristy A. Carpenter
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Xudong Huang
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
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22
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Zararsız G, Goksuluk D, Korkmaz S, Eldem V, Zararsiz GE, Duru IP, Ozturk A. A comprehensive simulation study on classification of RNA-Seq data. PLoS One 2017; 12:e0182507. [PMID: 28832679 PMCID: PMC5568128 DOI: 10.1371/journal.pone.0182507] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 07/19/2017] [Indexed: 02/02/2023] Open
Abstract
RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.
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Affiliation(s)
- Gökmen Zararsız
- Turcosa Analytics Solutions Ltd Co, Erciyes Teknopark, 38039, Kayseri, Turkey
- Department of Biostatistics, Erciyes University, Kayseri, Turkey
| | - Dincer Goksuluk
- Turcosa Analytics Solutions Ltd Co, Erciyes Teknopark, 38039, Kayseri, Turkey
- Department of Biostatistics, Hacettepe University, Ankara, Turkey
| | - Selcuk Korkmaz
- Turcosa Analytics Solutions Ltd Co, Erciyes Teknopark, 38039, Kayseri, Turkey
- Department of Biostatistics, Hacettepe University, Ankara, Turkey
| | - Vahap Eldem
- Department of Biology, Istanbul University, Istanbul, Turkey
| | | | | | - Ahmet Ozturk
- Department of Biostatistics, Erciyes University, Kayseri, Turkey
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Gally JM, Bourg S, Do QT, Aci-Sèche S, Bonnet P. VSPrep: A General KNIME Workflow for the Preparation of Molecules for Virtual Screening. Mol Inform 2017; 36. [PMID: 28586180 DOI: 10.1002/minf.201700023] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 05/05/2017] [Indexed: 12/27/2022]
Abstract
Over the past decades, virtual screening has proved itself to be a valuable asset to identify new bioactive compounds. The vast majority of commonly used techniques can be described in three steps: pre-processing the dataset i. e. small (ligands) and eventually larger (receptors) molecules, execute the method and finally analyse the results. Hence, the preparation of ligands is a critical step for success of commonly used virtual screening approaches such as protein-ligand docking, similarity or pharmacophore search. We present here a new workflow, VSPrep, for the pre-processing of small molecules; it is based on freely accessible tools for academics and is integrated within the KNIME platform. It can be used to perform several chemoinformatics tasks such as molecular database cleaning, tautomer and stereoisomer enumeration, focused library design and conformer generation. Additionally, graphical reports of the results are provided to the user as a convenient analysis tool.
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Affiliation(s)
- José-Manuel Gally
- Institut de Chimie Organique et Analytique (ICOA), Université d'Orléans et CNRS, UMR7311, BP 6759, 55067, Orléans, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), Université d'Orléans et CNRS, UMR7311, BP 6759, 55067, Orléans, France
| | - Quoc-Tuan Do
- Greenpharma SAS., 3, allée du Titane, 45100, Orléans, France
| | - Samia Aci-Sèche
- Institut de Chimie Organique et Analytique (ICOA), Université d'Orléans et CNRS, UMR7311, BP 6759, 55067, Orléans, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), Université d'Orléans et CNRS, UMR7311, BP 6759, 55067, Orléans, France
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Sari S, Karakurt A, Uslu H, Kaynak FB, Çalış Ü, Dalkara S. New (arylalkyl)azole derivatives showing anticonvulsant effects could have VGSC and/or GABA AR affinity according to molecular modeling studies. Eur J Med Chem 2016; 124:407-416. [PMID: 27597416 DOI: 10.1016/j.ejmech.2016.08.032] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 07/11/2016] [Accepted: 08/14/2016] [Indexed: 01/06/2023]
Abstract
(Arylalkyl)azoles (AAAs) emerged as a novel class of antiepileptic agents with the invention of nafimidone and denzimol. Several AAA derivatives with potent anticonvulsant activities have been reported so far, however neurotoxicity was usually an issue. We prepared a set of ester derivatives of 1-(2-naphthyl)-2-(1H-1,2,4-triazol-1-yl)ethanone oxime and evaluated their anticonvulsant and neurotoxic effects in mice. Most of our compounds were protective against maximal electroshock (MES)- and/or subcutaneous metrazol (s.c. MET)-induced seizures whereas none of them showed neurotoxicity. Nafimidone and denzimol have an activity profile similar to that of phenytoin or carbamazepine, both of which are known to inhibit voltage-gated sodium channels (VGSCs) as well as to enhance γ-aminobutiric acid (GABA)-mediated response. In order to get insights into the effects of our compounds on VGSCs and A-type GABA receptors (GABAARs) we performed docking studies using homology model of Na+ channel inner pore and GABAAR as docking scaffolds. We found that our compounds bind VGSCs in similar ways as phenytoin, carbamazepine, and lamotrigine. They showed strong affinity to benzodiazepine (BZD) binding site and their binding interactions were mainly complied with the experimental data and the reported BZD binding model.
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Affiliation(s)
- Suat Sari
- Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 06100, Ankara, Turkey
| | - Arzu Karakurt
- İnönü University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 44280, Malatya, Turkey.
| | - Harun Uslu
- İnönü University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 44280, Malatya, Turkey
| | - F Betül Kaynak
- Hacettepe University, Faculty of Engineering, Department of Physics Engineering, 06532, Ankara, Turkey
| | - Ünsal Çalış
- Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 06100, Ankara, Turkey
| | - Sevim Dalkara
- Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 06100, Ankara, Turkey
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