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Boonyarit B, Yamprasert N, Kaewnuratchadasorn P, Kinchagawat J, Prommin C, Rungrotmongkol T, Nutanong S. GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity. J Comput Chem 2024; 45:2001-2023. [PMID: 38713612 DOI: 10.1002/jcc.27388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/09/2024]
Abstract
The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.
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Affiliation(s)
- Bundit Boonyarit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Nattawin Yamprasert
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | | | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Chanatkran Prommin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
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Chang H, Zhang Z, Tian J, Bai T, Xiao Z, Wang D, Qiao R, Li C. Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors. ACS OMEGA 2024; 9:2314-2324. [PMID: 38250375 PMCID: PMC10795152 DOI: 10.1021/acsomega.3c06225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 01/23/2024]
Abstract
Epidermal growth factor receptor (EGFR) plays a pivotal regulatory role in treating patients with advanced nonsmall cell lung cancer (NSCLC). Following the emergence of the EGFR tertiary CIS C797S mutation, all types of inhibitors lose their inhibitory activity, necessitating the urgent development of new inhibitors. Computer systems employ machine learning methods to process substantial volumes of data and construct models that enable more accurate predictions of the outcomes of new inputs. The purpose of this article is to uncover innovative fourth-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with the aid of machine learning techniques. The paper's data set was high-dimensional and sparse, encompassing both structured and unstructured descriptors. To address this considerable challenge, we introduced a fusion framework to select critical molecule descriptors by integrating the full quadratic effect model and the Lasso model. Based on structural descriptors obtained from the full quadratic effect model, we conceived and synthesized a variety of small-molecule inhibitors. These inhibitors demonstrated potent inhibitory effects on the two mutated kinases L858R/T790M/C797S and Del19/T790M/C797S. Moreover, we applied our model to virtual screening, successfully identifying four hit compounds. We have evaluated these hit ADME characteristics and look forward to conducting activity evaluations on them in the future to discover a new generation of EGFR-TKI.
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Affiliation(s)
- Hao Chang
- State
Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Zeyu Zhang
- School
of Mathematics and Statistics, Beijing Institute
of Technology, Beijing 100081, P. R. China
| | - Jiaxin Tian
- State
Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Tian Bai
- School
of Mathematics and Statistics, Beijing Institute
of Technology, Beijing 100081, P. R. China
| | - Zijie Xiao
- State
Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Dianpeng Wang
- School
of Mathematics and Statistics, Beijing Institute
of Technology, Beijing 100081, P. R. China
| | - Renzhong Qiao
- State
Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
| | - Chao Li
- State
Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China
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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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Vetrivel A, Ramasamy J, Natchimuthu S, Senthil K, Ramasamy M, Murugesan R. Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa. J Biomol Struct Dyn 2022; 41:4124-4142. [PMID: 35451916 DOI: 10.1080/07391102.2022.2064331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Pseudomonas aeruginosa, a virulent pathogen affects patients with cystic fibrosis and nosocomial infections. Quorum sensing (QS) mechanism plays a crucial role in causing these ailments by mediating biofilm formation and expressing virulent genes. A novel approach to circumvent this bacterial infection is by hindering its QS network. Targeting LasR of las system serves beneficial as it holds the top position in QS system cascade. Here, we have integrated machine learning, pharmacophore based virtual screening, molecular docking and simulation studies to look for new leads as inhibitors for LasR. Support vector machine (SVM) learning algorithm was used to generate QSAR models from 66 antagonist dataset. The top three models resulted in correlation coefficient (R2) values of 0.67, 0.86, and 0.91, respectively. The correlation coefficient (R2test) values on external test set were found to be 0.62, 0.57, and 0.55, respectively. A four-point pharmacophore model was developed. The pharmacophore hypothesis AAAD_1 was used to screen for potential leads against MolPort database in ZincPharmer. The leads which showed predicted pIC50 value of >8.00 by SVM models were subjected to docking analysis that reranked the compounds based on docking scores. Four top leads namely ZINC3851967 N-[3,5-bis(trifluoromethyl)phenyl]-5-tert-butyl-6-chloropyrazine-2-carboxamide, ZINC4024175 4-Amino-1-[(2R,3S,4S,5S)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-2-oxopyrimidine-5-carbonitrile, ZINC2125703 N-[(5-Methoxy-4,7-dimethyl-2-oxo-2H-chromen-3-yl)acetyl]-beta-alanine, and ZINC3851966 N-[3,5-Bis(trifluoromethyl)phenyl]5-tert-butylpyrazine-2-carboxamide were selected. These compounds were checked for its stability by performing a molecular dynamics simulation for a period of 100 ns. The ADME properties of the leads were also determined. Hence, the compounds identified in this study can be used as possible leads for developing a novel inhibitor for LasR.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aishwarya Vetrivel
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Janani Ramasamy
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Santhi Natchimuthu
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Kalaiselvi Senthil
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Monica Ramasamy
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Rajeswari Murugesan
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
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5
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Nguyen L, Nguyen Vo TH, Trinh QH, Nguyen BH, Nguyen-Hoang PU, Le L, Nguyen BP. iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation. J Chem Inf Model 2022; 62:5080-5089. [PMID: 35157472 DOI: 10.1021/acs.jcim.1c00920] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.
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Affiliation(s)
- Loc Nguyen
- Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Thanh-Hoang Nguyen Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Quang H Trinh
- Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam.,School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Bach Hoai Nguyen
- School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Phuong-Uyen Nguyen-Hoang
- Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Ly Le
- Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam.,Vingroup Big Data Institute, Ha Noi 100000, Vietnam
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
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6
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Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2021.214101] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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7
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Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR. Mol Divers 2021; 26:1715-1730. [PMID: 34636023 DOI: 10.1007/s11030-021-10300-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.
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8
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Dhall A, Patiyal S, Sharma N, Devi NL, Raghava GPS. Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm. Comput Biol Med 2021; 137:104780. [PMID: 34450382 PMCID: PMC8378993 DOI: 10.1016/j.compbiomed.2021.104780] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/11/2021] [Accepted: 08/18/2021] [Indexed: 12/27/2022]
Abstract
Background Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway. Method The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models. Results The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset. Conclusion We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver “STAT3In” (https://webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Naorem Leimarembi Devi
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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Saini R, Fatima S, Agarwal SM. TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors. Chem Biol Drug Des 2021; 96:921-930. [PMID: 33058464 DOI: 10.1111/cbdd.13697] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 12/26/2022]
Abstract
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance-based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross-validation and independent validation datasets. It was observed that the random forest-based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open-source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti-TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.
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Affiliation(s)
- Ravi Saini
- School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, India
| | - Shehnaz Fatima
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
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Jiang X, Ren M, Shuang X, Yang H, Shi D, Lai Q, Dong Y. Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma. J Magn Reson Imaging 2021; 54:497-507. [PMID: 33638577 DOI: 10.1002/jmri.27579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions. PURPOSE To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM. STUDY TYPE Retrospective. POPULATION A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set). FIELD STRENGTH/SEQUENCE T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T. ASSESSMENT Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features. STATISTICAL TESTS The Mann-Whitney U test and χ2 test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results. RESULTS The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models. DATA CONCLUSION Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Xiran Jiang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Meihong Ren
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Xue Shuang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Huazhe Yang
- Department of Biophysics, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Dabao Shi
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Qingyuan Lai
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
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11
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Kumar M, Joshi G, Chatterjee J, Kumar R. Epidermal Growth Factor Receptor and its Trafficking Regulation by Acetylation: Implication in Resistance and Exploring the Newer Therapeutic Avenues in Cancer. Curr Top Med Chem 2021; 20:1105-1123. [PMID: 32031073 DOI: 10.2174/1568026620666200207100227] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 01/17/2020] [Accepted: 01/24/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND The EGFR is overexpressed in numerous cancers. So, it becomes one of the most favorable drug targets. Single-acting EGFR inhibitors on prolong use induce resistance and side effects. Inhibition of EGFR and/or its interacting proteins by dual/combined/multitargeted therapies can deliver more efficacious drugs with less or no resistance. OBJECTIVE The review delves deeper to cover the aspects of EGFR mediated endocytosis, leading to its trafficking, internalization, and crosstalk(s) with HDACs. METHODS AND RESULTS This review is put forth to congregate relevant literature evidenced on EGFR, its impact on cancer prognosis, inhibitors, and its trafficking regulation by acetylation along with the current strategies involved in targeting these proteins (EGFR and HDACs) successfully by involving dual/hybrid/combination chemotherapy. CONCLUSION The current information on cross-talk of EGFR and HDACs would likely assist researchers in designing and developing dual or multitargeted inhibitors through combining the required pharmacophores.
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Affiliation(s)
- Manvendra Kumar
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, School of Basic and Applied Sciences, Central University of Punjab, Bathinda, 151001, India
| | - Gaurav Joshi
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, School of Basic and Applied Sciences, Central University of Punjab, Bathinda, 151001, India
| | - Joydeep Chatterjee
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, School of Basic and Applied Sciences, Central University of Punjab, Bathinda, 151001, India
| | - Raj Kumar
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, School of Basic and Applied Sciences, Central University of Punjab, Bathinda, 151001, India
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12
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QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models. Med Chem Res 2019. [DOI: 10.1007/s00044-019-02437-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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13
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Rajput A, Kumar A, Kumar M. Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus. Front Pharmacol 2019; 10:71. [PMID: 30809147 PMCID: PMC6379726 DOI: 10.3389/fphar.2019.00071] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 01/21/2019] [Indexed: 12/26/2022] Open
Abstract
Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC50). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/.
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Affiliation(s)
- Akanksha Rajput
- Virology Discovery Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Archit Kumar
- Virology Discovery Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Manoj Kumar
- Virology Discovery Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
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Sharma A, Sharma S, Gupta M, Fatima S, Saini R, Agarwal SM. Pharmacokinetic profiling of anticancer phytocompounds using computational approach. PHYTOCHEMICAL ANALYSIS : PCA 2018; 29:559-568. [PMID: 29667756 DOI: 10.1002/pca.2767] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 02/15/2018] [Accepted: 02/17/2018] [Indexed: 06/08/2023]
Abstract
INTRODUCTION Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process. OBJECTIVE With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability. METHODOLOGY Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors. RESULTS The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity. CONCLUSION We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.
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Affiliation(s)
- Ashish Sharma
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Shilpa Sharma
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Mansi Gupta
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Shehnaz Fatima
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Ravi Saini
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
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15
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Fatima S, Agarwal SM. Unraveling structural requirements of amino-pyrimidine T790M/L858R double mutant EGFR inhibitors: 2D and 3D QSAR study. J Recept Signal Transduct Res 2018; 38:299-306. [DOI: 10.1080/10799893.2018.1494740] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Shehnaz Fatima
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
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16
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Fatima S, Gupta P, Agarwal SM. Insight into structural requirements of antiamoebic flavonoids: 3D-QSAR and G-QSAR studies. Chem Biol Drug Des 2018; 92:1743-1749. [DOI: 10.1111/cbdd.13343] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/03/2018] [Accepted: 05/12/2018] [Indexed: 01/05/2023]
Affiliation(s)
- Shehnaz Fatima
- Bioinformatics Division; ICMR-National Institute of Cancer Prevention and Research; Noida India
| | - Payal Gupta
- Bioinformatics Division; ICMR-National Institute of Cancer Prevention and Research; Noida India
| | - Subhash Mohan Agarwal
- Bioinformatics Division; ICMR-National Institute of Cancer Prevention and Research; Noida India
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17
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Anoosha P, Sakthivel R, Gromiha MM. Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer. Mutat Res 2017; 806:19-26. [PMID: 28938109 DOI: 10.1016/j.mrfmmm.2017.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/21/2017] [Accepted: 08/22/2017] [Indexed: 06/07/2023]
Abstract
Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.
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Affiliation(s)
- P Anoosha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.
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Speck-Planche A, Dias Soeiro Cordeiro MN. Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads. ACS COMBINATORIAL SCIENCE 2017; 19:501-512. [PMID: 28437091 DOI: 10.1021/acscombsci.7b00039] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski's rule of five and its variants.
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Affiliation(s)
- Alejandro Speck-Planche
- LAQV@REQUIMTE/Department
of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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19
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Abstract
A series of indeno[1,2-c]quinoline derivatives were designed, synthesized and evaluated for their anti-tuberculosis (anti-TB) and anti-inflammatory activities. The minimum inhibitory concentration (MIC) of the newly synthesized compound was tested against Mycobacterium tuberculosis H37RV. Among the tested compounds, (E)-N′-[6-(4-hydroxypiperidin-1-yl)-11H-indeno[1,2-c]quinolin-11-ylidene]isonicotino-hydrazide (12), exhibited significant activities against the growth of M. tuberculosis (MIC values of 0.96 μg/mL) with a potency approximately equal to that of isoniazid (INH), an anti-TB drug. Important structure features were analyzed by quantitative structure–activity relationship (QSAR) analysis to give better insights into the structure determinants for predicting the anti-TB activity. The anti-inflammatory activity was induced by superoxide anion generation and neutrophil elastase (NE) release using the formyl-l-methionyl-l-leucyl-l-phenylalanine (fMLF)-activated human neutrophils method. Results indicated that compound 12 demonstrated a potent dual inhibitory effect on NE release and superoxide anion generation with IC50 values of 1.76 and 1.72 μM, respectively. Our results indicated that compound 12 is a potential lead compound for the discovery of dual anti-TB and anti-inflammatory drug candidates. In addition, 6-[3-(hydroxymethyl)piperidin-1-yl]-9-methoxy-11H-indeno[1,2-c]quinolin-11-one (4g) showed a potent dual inhibitory effect on NE release and superoxide anion generation with IC50 values of 0.46 and 0.68 μM, respectively, and is a potential lead compound for the discovery of anti-inflammatory drug candidates.
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20
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Abbasi M, Sadeghi-Aliabadi H, Amanlou M. 3D-QSAR, molecular docking, and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold. J Biomol Struct Dyn 2017; 36:1463-1478. [PMID: 28482755 DOI: 10.1080/07391102.2017.1326319] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.
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Affiliation(s)
- Maryam Abbasi
- a Faculty of Pharmacy, Department of Medicinal Chemistry , Isfahan University of Medical Sciences , 81746-73461 Isfahan , Iran
| | - Hojjat Sadeghi-Aliabadi
- a Faculty of Pharmacy, Department of Medicinal Chemistry , Isfahan University of Medical Sciences , 81746-73461 Isfahan , Iran
| | - Massoud Amanlou
- b Faculty of Pharmacy, Department of Medicinal Chemistry , Drug Design and Development Research Center, Tehran University of Medical Sciences , Tehran , Iran
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21
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Qureshi A, Kaur G, Kumar M. AVCpred: an integrated web server for prediction and design of antiviral compounds. Chem Biol Drug Des 2017; 89:74-83. [PMID: 27490990 PMCID: PMC7162012 DOI: 10.1111/cbdd.12834] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 07/21/2016] [Accepted: 07/25/2016] [Indexed: 12/11/2022]
Abstract
Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.
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Affiliation(s)
- Abid Qureshi
- Bioinformatics CentreInstitute of Microbial TechnologyCouncil of Scientific and Industrial ResearchChandigarhIndia
| | - Gazaldeep Kaur
- Bioinformatics CentreInstitute of Microbial TechnologyCouncil of Scientific and Industrial ResearchChandigarhIndia
| | - Manoj Kumar
- Bioinformatics CentreInstitute of Microbial TechnologyCouncil of Scientific and Industrial ResearchChandigarhIndia
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22
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Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GPS. Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer 2016; 16:77. [PMID: 26860193 PMCID: PMC4748564 DOI: 10.1186/s12885-016-2082-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 01/21/2016] [Indexed: 11/16/2022] Open
Abstract
Background In past, numerous quantitative structure-activity relationship (QSAR) based models have been developed for predicting anticancer activity for a specific class of molecules against different cancer drug targets. In contrast, limited attempt have been made to predict the anticancer activity of a diverse class of chemicals against a wide variety of cancer cell lines. In this study, we described a hybrid method developed on thousands of anticancer and non-anticancer molecules tested against National Cancer Institute (NCI) 60 cancer cell lines. Results Our analysis of anticancer molecules revealed that majority of anticancer molecules contains 18–24 carbon atoms and are dominated by functional groups like R2NH, R3N, ROH, RCOR, and ROR. It was also observed that certain substructures (e.g., 1-methoxy-4-methylbenzene, 1-methoxy benzene, Nitrobenzene, Indole, Propenyl benzene) are more abundant in anticancer molecules. Next, we developed anticancer molecule prediction models using various machine-learning techniques and achieved maximum matthews correlation coefficient (MCC) of 0.81 with 90.40 % accuracy using support vector machine (SVM) based models. In another approach, a novel similarity or potency score based method has been developed using selected fragments/fingerprints and achieved maximum MCC of 0.82 with 90.65 % accuracy. Finally, we combined the strength of above methods and developed a hybrid method with maximum MCC of 0.85 with 92.47 % accuracy. Conclusions We developed a hybrid method utilizing the best of machine learning and potency score based method. The highly accurate hybrid method can be used for classification of anticancer and non-anticancer molecules. In order to facilitate scientific community working in the field of anticancer drug discovery, we integrate hybrid and potency method in a web server CancerIN. This server provides various facilities that includes; virtual screening of anticancer molecules, analog based drug design, and similarity with known anticancer molecules (http://crdd.osdd.net/oscadd/cancerin). Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2082-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Harinder Singh
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Rahul Kumar
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Sandeep Singh
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Kumardeep Chaudhary
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Ankur Gautam
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Gajendra P S Raghava
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
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23
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Abstract
INTRODUCTION The hybridization of biologically active molecules is a powerful tool for drug discovery used to target a variety of diseases. It offers the prospect of better drugs for the treatment of a number of illnesses including cancer, malaria, tuberculosis and AIDS. Hybrid drugs can provide combination therapies in a single multi-functional agent and, by doing so, be more specific and powerful than conventional classic treatments. This research field is in great expansion and attracts many researchers worldwide. AREA COVERED This review covers the main research published between early 2013 to mid-2015 and takes into account several previous reviews on the subject. Its intention is to showcase the most recent advances reported towards the development of molecular hybrids in drug discovery. Particular attention is given to anticancer hybrids throughout the review. EXPERT OPINION Current advances show that molecular hybrids of biologically active molecules can lead to powerful therapeutics. Natural products play a key role in this field. It is also believed that toxin hybrids present a great opportunity for future progress and should be further explored. Furthermore, the synthesis of hybrid organometallics should be systematically studied as it can lead to potent drugs. The crucial requirement for growth still remains the efficacy of synthesis. Hence, the development of efficient synthetic methods allowing rapid access to diverse series of hybrids must be further investigated by researchers.
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Affiliation(s)
- Gervais Bérubé
- a Département de Chimie, Biochimie et Physique , Université du Québec à Trois-Rivières , Québec , Canada
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24
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Dhiman K, Agarwal SM. NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors. RSC Adv 2016. [DOI: 10.1039/c6ra02772e] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Prediction of naturally occurring plant based compounds as anticancer agents is the key to developing new chemical entities in the area of therapeutic oncology. A webserver for assessing anticancer potential of phytomolecules has been developed.
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Affiliation(s)
- Kanika Dhiman
- Bioinformatics Division
- Institute of Cytology and Preventive Oncology
- Noida-201301
- India
| | - Subhash Mohan Agarwal
- Bioinformatics Division
- Institute of Cytology and Preventive Oncology
- Noida-201301
- India
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25
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Abstract
Volatile organic compounds in cancer database (VOCC) has been developed, which provides comprehensive information of VOCs distinctly observed in cancer vs. normal from various malignancies and different sources.
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Affiliation(s)
- Subhash Mohan Agarwal
- Bioinformatics Division
- National Institute of Cancer Prevention and Research (NICPR-ICMR)
- Noida – 201301
- India
| | - Mansi Sharma
- Bioinformatics Division
- National Institute of Cancer Prevention and Research (NICPR-ICMR)
- Noida – 201301
- India
| | - Shehnaz Fatima
- Bioinformatics Division
- National Institute of Cancer Prevention and Research (NICPR-ICMR)
- Noida – 201301
- India
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26
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Sharma VK, Nandekar PP, Sangamwar A, Pérez-Sánchez H, Agarwal SM. Structure guided design and binding analysis of EGFR inhibiting analogues of erlotinib and AEE788 using ensemble docking, molecular dynamics and MM-GBSA. RSC Adv 2016. [DOI: 10.1039/c6ra08517b] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The study uncovers an essential pharmacophoric requirement for design of new EGFR inhibitors. Docking and MD simulation confirmed that the occupancy of an additional sub-pocket in the EGFR active site is important for tight EGFR-inhibitor binding.
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Affiliation(s)
- Vishnu K. Sharma
- Department of Pharmacoinformatics
- National Institute of Pharmaceutical and Education Research (NIPER)
- India
| | - Prajwal P. Nandekar
- Department of Pharmacoinformatics
- National Institute of Pharmaceutical and Education Research (NIPER)
- India
| | - Abhay Sangamwar
- Department of Pharmacoinformatics
- National Institute of Pharmaceutical and Education Research (NIPER)
- India
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC)
- Universidad Católica San Antonio de Murcia (UCAM)
- Spain
| | - Subhash Mohan Agarwal
- Bioinformatics Division
- Institute of Cytology and Preventive Oncology
- Noida-201301
- India
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Dearden JC, Hewitt M, Roberts DW, Enoch SJ, Rowe PH, Przybylak KR, Vaughan-Williams GD, Smith ML, Pillai GG, Katritzky AR. Mechanism-Based QSAR Modeling of Skin Sensitization. Chem Res Toxicol 2015; 28:1975-86. [PMID: 26382665 DOI: 10.1021/acs.chemrestox.5b00197] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.
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Affiliation(s)
- J C Dearden
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - M Hewitt
- School of Pharmacy, University of Wolverhampton , Wulfruna Street, Wolverhampton WV1 1LY, United Kingdom
| | - D W Roberts
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - S J Enoch
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - P H Rowe
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - K R Przybylak
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - G D Vaughan-Williams
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - M L Smith
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, United Kingdom
| | - G G Pillai
- Department of Chemistry, University of Florida , Gainsville, Florida 32611-7200, United States.,Institute of Chemistry, University of Tartu , 50411 Tartu, Estonia
| | - A R Katritzky
- Department of Chemistry, University of Florida , Gainsville, Florida 32611-7200, United States
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Singh H, Singh S, Singla D, Agarwal SM, Raghava GPS. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol Direct 2015; 10:10. [PMID: 25880749 PMCID: PMC4372225 DOI: 10.1186/s13062-015-0046-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 03/06/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole. RESULTS We train, test and validate our classification models on a dataset called EGFR10 that contains 508 inhibitors (having inhibition activity IC50 less than 10 nM) and 2997 non-inhibitors. Our Random forest based model achieved maximum MCC 0.49 with accuracy 83.7% on a validation set using 881 PubChem fingerprints. In this study, frequency-based feature selection technique has been used to identify best fingerprints. It was observed that PubChem fingerprints FP380 (C(~O) (~O)), FP579 (O = C-C-C-C), FP388 (C(:C) (:N) (:N)) and FP 816 (ClC1CC(Br)CCC1) are more frequent in the inhibitors in comparison to non-inhibitors. In addition, we created different datasets namely EGFR100 containing inhibitors having IC50 < 100 nM and EGFR1000 containing inhibitors having IC50 < 1000 nM. We trained, test and validate our models on datasets EGFR100 and EGFR1000 datasets and achieved and maximum MCC 0.58 and 0.71 respectively. In addition, models were developed for predicting quinazoline and pyrimidine based EGFR inhibitors. CONCLUSIONS In summary, models have been developed on a large set of molecules of various classes for discriminating EGFR inhibitors and non-inhibitors. These highly accurate prediction models can be used to design and discover novel EGFR inhibitors. In order to provide service to the scientific community, a web server/standalone EGFRpred also has been developed ( http://crdd.osdd.net/oscadd/egfrpred/ ).
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Affiliation(s)
- Harinder Singh
- Bioinformatics Center, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Sandeep Singh
- Bioinformatics Center, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Deepak Singla
- Bioinformatics Center, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Subhash M Agarwal
- Institute of Cytology and Preventive Oncology, Sector 39, Noida, 201301, Uttar Pradesh, India.
| | - Gajendra P S Raghava
- Bioinformatics Center, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
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