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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, Ghosh A, Masand VH, Ming LC, Rashid S. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain. J Biomol Struct Dyn 2024; 42:2550-2569. [PMID: 37144753 DOI: 10.1080/07391102.2023.2205948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/17/2023] [Indexed: 05/06/2023]
Abstract
Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata, India
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Vijay H Masand
- Department of Chemistry, Vidyabharati Mahavidyalalya, Amravati, Maharashtra, India
| | - Long Chiau Ming
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Masand V, Humane V, Mali S, Alzahrani AYA, Rashid S, Elossaily GM. Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in- vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor. J Biomol Struct Dyn 2024:1-31. [PMID: 38385447 DOI: 10.1080/07391102.2024.2319104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024]
Abstract
A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay Masand
- Department of Chemistry, Amravati, Maharashtra, India
| | - Vivek Humane
- Department of Chemistry, Shri R. R. Lahoti Science college, Morshi District: Amravati, Maharashtra, India
| | - Suraj Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Gehan M Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
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Masand VH, Al-Hussain SA, Rathore MM, Thakur SD, Akasapu S, Samad A, Al-Mutairi AA, Zaki MEA. Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B. Int J Mol Sci 2022; 23:ijms232314527. [PMID: 36498857 PMCID: PMC9739353 DOI: 10.3390/ijms232314527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/01/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R2tr = 0.815, Q2LMO = 0.808, R2ex = 0.814, CCCex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.
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Affiliation(s)
- Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
- Correspondence: (V.H.M.); (M.E.A.Z.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Mithilesh M. Rathore
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
| | - Sumer D. Thakur
- Department of Chemistry, RDIK and NKD College, Badnera, Amravati 444701, Maharashtra, India
| | | | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Aamal A. Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
- Correspondence: (V.H.M.); (M.E.A.Z.)
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Jawarkar RD, Sharma P, Jain N, Gandhi A, Mukerjee N, Al-Mutairi AA, Zaki MEA, Al-Hussain SA, Samad A, Masand VH, Ghosh A, Bakal RL. QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads. Molecules 2022; 27:molecules27154951. [PMID: 35956900 PMCID: PMC9370430 DOI: 10.3390/molecules27154951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 12/04/2022] Open
Abstract
ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.
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Affiliation(s)
- Rahul D. Jawarkar
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
- Correspondence: (R.D.J.); (M.E.A.Z.); Tel.: +91-7385178762 (R.D.J.)
| | - Praveen Sharma
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
| | - Neetesh Jain
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
| | - Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431004, Maharashtra, India;
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata 700118, West Bengal, India;
| | - Aamal A. Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
- Correspondence: (R.D.J.); (M.E.A.Z.); Tel.: +91-7385178762 (R.D.J.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Kurdistan Region, Iraq;
| | - Vijay H. Masand
- Department of Chemistry, Vidyabharati Mahavidyalalya, Camp Road, Amravati 444602, Maharashtra, India;
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati 781014, Assam, India;
| | - Ravindra L. Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati 444603, Maharashtra, India;
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Bukhari SNA, Elsherif MA, Junaid K, Ejaz H, Alam P, Samad A, Jawarkar RD, Masand VH. Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis. Pharmaceuticals (Basel) 2022; 15:ph15070834. [PMID: 35890133 PMCID: PMC9316833 DOI: 10.3390/ph15070834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/12/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer’s disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure–activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R2tr = 0.78, R2ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.
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Affiliation(s)
- Syed Nasir Abbas Bukhari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Saudi Arabia
| | | | - Kashaf Junaid
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Hasan Ejaz
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Pravej Alam
- Department of Biology, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati 444603, Maharashtra, India
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
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Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures. Pharmaceuticals (Basel) 2022; 15:ph15060745. [PMID: 35745664 PMCID: PMC9231298 DOI: 10.3390/ph15060745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R2tr = 0.76, Q2LMO = 0.76, and R2ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.
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Ghosh A, Mukerjee N, Sharma B, Pant A, Kishore Mohanta Y, Jawarkar RD, Bakal RL, Terefe EM, Batiha GES, Mostafa-Hedeab G, Aref Albezrah NK, Dey A, Baishya D. Target Specific Inhibition of Protein Tyrosine Kinase in Conjunction With Cancer and SARS-COV-2 by Olive Nutraceuticals. Front Pharmacol 2022; 12:812565. [PMID: 35356629 PMCID: PMC8959131 DOI: 10.3389/fphar.2021.812565] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/24/2021] [Indexed: 12/18/2022] Open
Abstract
The fact that viruses cause human cancer dates back to the early 1980s. By reprogramming cellular signaling pathways, viruses encoded protein that can regulate altered control of cell cycle events. Viruses can interact with a superfamily of membrane bound protein, receptor tyrosine kinase to modulate their activity in order to increase virus entrance into cells and promotion of viral replication within the host. Therefore, our study aimed at screening of inhibitors of tyrosine kinase using natural compounds from olive. Protein tyrosine kinase (PTK) is an important factor for cancer progression and can be linked to coronavirus. It is evident that over expression of Protein tyrosine kinase (PTK) enhance viral endocytosis and proliferation and the use of tyrosine kinase inhibitors reduced the period of infection period. Functional network studies were carried out using two major PTKs viz. Anaplastic lymphoma kinase (ALK) and B-lymphocytic kinase (BTK). They are associated with coronavirus in regulation of cell signaling proteins for cellular processes. We virtually screened for 161 library of natural compounds from olive found overexpressed in ALK and BTK in metastatic as well as virus host cells. We have employed both ligand and target-based approach for drug designing by high throughput screening using Multilinear regression model based QSAR and docking. The QSAR based virtual screening of 161 olive nutraceutical compounds has successfully identified certain new hit; Wedelosin, in which, the descriptor rsa (ratio of molecular surface area to the solvent accessible surface area) plays crucial role in deciding Wedelosin’s inhibitory potency. The best-docked olive nutraceuticals further investigated for the stability and effectivity of the BTK and ALK during in 150 ns molecular dynamics and simulation. Post simulation analysis and binding energy estimation in MMGBSA further revealed the intensive potential of the olive nutraceuticals in PTK inhibition. This study is therefore expected to widen the use of nutraceuticals from olive in cancer as well as SARS-CoV2 alternative therapy.
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Affiliation(s)
- Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata, India
| | - Bhavdeep Sharma
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Anushree Pant
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Yugal Kishore Mohanta
- Department of Applied Biology, University of Science and Technology Meghalaya, Ri Bhoi, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Ravindrakumar L Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Ermias Mergia Terefe
- Department of Pharmacology and Pharmacognosy, School of Pharmacy and Health Sciences, United states International University-Africa, Nairobi, Kenya
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Gomaa Mostafa-Hedeab
- Pharmacology Department & Health Research Unit-Medical College-Jouf University, Sakakah, Saudi Arabia.,Pharmacology Department, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | | | - Abhijit Dey
- Department of Life Sciences, Presidency University, Kolkata, India
| | - Debabrat Baishya
- Department of Bioengineering, Gauhati University, Guwahati, India
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Hung TNK, Le NQK, Le NH, Tuan LV, Nguyen TP, Thi C, Kang JH. An AI-based prediction model for drug-drug interactions in osteoporosis and Paget's diseases from SMILES. Mol Inform 2022; 41:e2100264. [PMID: 34989149 DOI: 10.1002/minf.202100264] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/05/2022] [Indexed: 11/06/2022]
Abstract
Referring to common skeletal-related diseases, osteoporosis and Paget's are two of the most frequently found diseases in the elderly. Nowadays, the combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which contains various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, furthermore, to mitigate cost and time in implementing the best combination of medications in clinical practice. Our dataset was collected from the DrugBank database, and we then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms have been implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74% in predicting DDIs. It was superior to individual models and previous methods in most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.
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Affiliation(s)
| | | | | | | | | | - Cao Thi
- University of Medicine and Pharmacy at Ho Chi Minh City, VIET NAM
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Jawarkar R, Bakal RL, Zaki ME, Al-Hussain S, Ghosh A, Gandhi A, Mukerjee N, Samad A, Masand VH, Lewaa I. QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CL pro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches. ARAB J CHEM 2022; 15:103499. [PMID: 34909066 PMCID: PMC8524701 DOI: 10.1016/j.arabjc.2021.103499] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 10/10/2021] [Indexed: 12/26/2022] Open
Abstract
Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.
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Key Words
- 3CLpro, 3C like Protease
- FDA, Food and Drug Administration
- GA-MLR
- GA-MLR, Genetic Algorithm Multilinear Regression
- HCoV SARS 3CLpro
- HCoV-HKU1, Human coronavirus HKU1
- HCoV-NL63, Human coronavirus NL63
- HCoVs, human coronaviruses
- Lead
- MD, Molecular Docking
- MDS, molecular dynamic simulation
- MERS, Middle East Respiratory Syndrome
- MMGBSA calculations
- MMGBSA, Molecular Mechanics Generalized Born and Surface Area
- Molecular docking and MD simulation
- OECD, Organization for Economic Corporation and Development
- QSAR based virtual screening
- QSAR, Quantitative Structure Activity Relationship
- RNA, Ribo-nucleic acid
- SARS, severe acute respiratory sign
- VS, Virtual Screening
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Affiliation(s)
- R.D. Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, 444603, India,Corresponding authors at: Department of Medicinal Chemistry, Dr. Rajendra Gode College of Pharmacy, Mardi Road, Amravati, Maharashtra, India and (Rahul D. Jawarkar). Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia (Magdi E.A. Zaki)
| | - Ravindrakumar L. Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, 444603, India
| | - Magdi E.A. Zaki
- Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia
| | - Sami Al-Hussain
- Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia,Corresponding authors at: Department of Medicinal Chemistry, Dr. Rajendra Gode College of Pharmacy, Mardi Road, Amravati, Maharashtra, India and (Rahul D. Jawarkar). Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic university, Riyadh 13318, Saudi Arabia (Magdi E.A. Zaki)
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam 781014, India
| | - Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra 431 004, India
| | - Nobendu Mukerjee
- Department of Microbiology; Ramakrishna Mission Vivekananda Centenary College, Akhil Mukherjee Rd, Chowdhary Para, Rahara, Khardaha, Kolkata, West Bengal 700118, India
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay H. Masand
- Department of Chemistry, Vidyabharti Mahavidyalaya, Camp Road, Amravati Maharashtra, India
| | - Israa Lewaa
- Department of Business Administration, Faculty of Business Administration, Economics & Political Science, The British University in Egypt (BUE), Cairo, Egypt
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10
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Shin HK. Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds. ACS OMEGA 2021; 6:35757-35768. [PMID: 34984306 PMCID: PMC8717557 DOI: 10.1021/acsomega.1c05693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/08/2021] [Indexed: 05/15/2023]
Abstract
Deep learning (DL) models in quantitative structure-activity relationship fed the molecular structure directly to the network without using human-designed descriptors by representing molecule as a graph or string (e.g., SMILES code). However, these two representations were oversimplification of real molecules to reflect chemical properties of molecular structures. Given that the choice of molecular representation determines the architecture of the DL model to apply, a novel way of molecular representation can open a way to apply diverse DL networks developed and used in other fields. A topological distance-based electron interaction (TDEi) tensor has been developed in this study inspired by the quantum mechanical model of the molecule, which defines a molecule with electrons and protons. In the TDEi tensor, the atomic orbital (AO) of each atom is represented by an electron configuration (EC) vector, which is a bit string based on the presence and absence of electrons in each AO according to spin indicated by positive and negative signs. Interactions between EC vectors were calculated based on the topological distance between atoms in a molecule. As a molecular structure was translated into 3D array, CNN models (modified VGGNet) were applied using a TDEi tensor to predict four physicochemical properties of drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127), and Freesolv (639). Models achieved good prediction accuracy. PCA showed that a stronger correlation was observed between the extracted features and the target endpoint as features were extracted from the deeper layer.
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Affiliation(s)
- Hyun Kil Shin
- Department
of Predictive Toxicology, Korea Institute
of Toxicology, Daejeon 34114, Republic of Korea
- Human
and Environmental Toxicology, University
of Science and Technology, Daejeon 34113, Republic of Korea
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11
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Jawarkar RD, Bakal RL, Khatale PN, Lewaa I, Jain CM, Manwar JV, Jaiswal MS. QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA. FUTURE JOURNAL OF PHARMACEUTICAL SCIENCES 2021. [DOI: 10.1186/s43094-021-00380-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA are amongst the favorable target for researchers to discover novel and more effective anticancer agents. To understand and elucidate structure activity relationship and mechanism of inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA, a QSAR modeling and molecular docking were conducted.
Results
Two robust QSAR model were obtained which consist of full set QSAR model (R2: 0.8174, CCCtr: 0.8995, Q2loo: 0.7881, Q2LMO: 0.7814) and divided set QSAR model (R2: 0.8217, CCCtr: 0.9021, Q2loo: 0.7886, Q2LMO: 0.7783, Q2-F1: 0.7078, Q2-F2: 0.6865, Q2-F3: 0.7346) for envisaging the inhibitory activity of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The analysis reveals that carbon atom exactly at 3 bonds from aromatic carbon atom, nitrogen atom exactly at six bonds from planer nitrogen atom, aromatic carbon atom within 2 A0 from the center of mass of molecule and occurrence of element hydrogen within 2 A0 from donar atom are the key pharmacophoric features important for dual inhibition of TERT and human telomeric G-quadruplex DNA. To validate this analysis, pharmacophore modeling and the molecular docking is performed. Molecular docking analysis support QSAR analysis and revealed that, dual inhibition of TERT and human telomeric DNA is mainly contributed from hydrophobic and hydrogen bonding interactions.
Conclusion
The findings of molecular docking, pharmacophore modelling, and QSAR are all consistent and in strong agreement. The validated QSAR analyses can detect structural alerts, pharmacophore modelling can classify a molecule's consensus pharmacophore involving hydrophobic and acceptor regions, whereas docking analysis can reveal the mechanism of dual inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The combination of QSAR, pharmacophore modeling and molecular docking may be useful for the future drug design of dual inhibitors to combat the devastating issue of resistance.
Graphical abstract
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12
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Gandhi A, Masand V, Zaki MEA, Al-Hussain SA, Ghorbal AB, Chapolikar A. QSAR analysis of sodium glucose co-transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:731-744. [PMID: 34494464 DOI: 10.1080/1062936x.2021.1971295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
QSAR (Quantitative Structure Activity Relationship) modelling was performed on a dataset of 90 sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors. The quantitative and explicative evaluations revealed some of the subtle and distinguished structural features that are responsible for the inhibitory potency of these compounds against SGLT2, such as less possible number of ring carbons at 8 Å from the lipophilic atoms in the molecule (fringClipo8A) and more possible value for the sum of the partial charges of the lipophilic atoms present within seven bonds from the donor atoms (lipo_don_7Bc). Multivariate GA-MLR (genetic algorithm-multi linear regression) and thorough validation methodology out-turned a statistically robust QSAR model with a very high predictability shown from various statistical parameters. A QSAR model with r2 = 0.83, F = 51.54, Q2LOO = 0.79, Q2LMO = 0.79, CCCcv = 0.88, Q2Fn = 0.76-0.81, r2ext = 0.77, CCCext = 0.85, and with RMSEtr < RMSEcv was proposed. This QSAR model will assist synthetic chemists in the development of the SGLT2 inhibitors as the antidiabetic leads.
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Affiliation(s)
- A Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India
| | - V Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, India
| | - M E A Zaki
- Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - S A Al-Hussain
- Department of Chemistry, College of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - A Ben Ghorbal
- Department of Mathematics and Statistics, College of Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - A Chapolikar
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, India
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13
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Gandhi A, Masand V, Zaki MEA, Al-Hussain SA, Ghorbal AB, Chapolikar A. Quantitative Structure-Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads. Molecules 2021; 26:molecules26164795. [PMID: 34443383 PMCID: PMC8401583 DOI: 10.3390/molecules26164795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 11/23/2022] Open
Abstract
In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.
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Affiliation(s)
- Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431 004, Maharashtra, India;
- Correspondence: (A.G.); (M.E.A.Z.)
| | - Vijay Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India;
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
- Correspondence: (A.G.); (M.E.A.Z.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Anis Ben Ghorbal
- Department of Mathematics and Statistics, College of Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Archana Chapolikar
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431 004, Maharashtra, India;
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14
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Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity. Int J Mol Sci 2021; 22:ijms22158352. [PMID: 34361118 PMCID: PMC8348508 DOI: 10.3390/ijms22158352] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/24/2021] [Accepted: 07/31/2021] [Indexed: 12/02/2022] Open
Abstract
Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.
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Scior T, Abdallah HH, Salvador-Atonal K, Laufer S. Dapsone is not a Pharmacodynamic Lead Compound for its Aryl Derivatives. Curr Comput Aided Drug Des 2021; 16:327-339. [PMID: 32507104 DOI: 10.2174/1573409915666191010104527] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 09/11/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND The relatedness between the linear equations of thermodynamics and QSAR was studied thanks to the recently elucidated crystal structure complexes between sulfonamide pterin conjugates and dihydropteroate synthase (DHPS) together with a published set of thirty- six synthetic dapsone derivatives with their reported entropy-driven activity data. Only a few congeners were slightly better than dapsone. OBJECTIVE Our study aimed at demonstrating the applicability of thermodynamic QSAR and to shed light on the mechanistic aspects of sulfone binding to DHPS. METHODS To this end ligand docking to DHPS, quantum mechanical properties, 2D- and 3D-QSAR as well as Principle Component Analysis (PCA) were carried out. RESULTS The short aryl substituents of the docked pterin-sulfa conjugates were outward oriented into the solvent space without interacting with target residues which explains why binding enthalpy (ΔH) did not correlate with potency. PCA revealed how chemically informative descriptors are evenly loaded on the first three PCs (interpreted as ΔG, ΔH and ΔS), while chemically cryptic ones reflected higher dimensional (complex) loadings. CONCLUSION It is safe to utter that synthesis efforts to introduce short side chains for aryl derivatization of the dapsone scaffold have failed in the past. On theoretical grounds we provide computed evidence why dapsone is not a pharmacodynamic lead for drug profiling because enthalpic terms do not change significantly at the moment of ligand binding to target.
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Affiliation(s)
- Thomas Scior
- Chemical Science Faculty, Benemerita Universidad Autonoma de Puebla, C.P. 72570, Puebla, Mexico
| | - Hassan H Abdallah
- Chemistry Department, College of Education, Salahaddin University, Erbil, Iraq.,Pharmacy School, University Sains Malaysia, USM, 11800, Penang, Malaysia
| | - Kenia Salvador-Atonal
- Chemical Science Faculty, Benemerita Universidad Autonoma de Puebla, C.P. 72570, Puebla, Mexico
| | - Stefan Laufer
- Pharmazeutisches Institut, Eberhard Karls Universität Tübingen, Tübingen, Germany
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16
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Zaki MEA, Al-Hussain SA, Masand VH, Akasapu S, Lewaa I. QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors. Molecules 2021; 26:molecules26071834. [PMID: 33805223 PMCID: PMC8038050 DOI: 10.3390/molecules26071834] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/12/2021] [Accepted: 03/19/2021] [Indexed: 11/24/2022] Open
Abstract
N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R2 = 0.77–0.79, Q2LMO = 0.75–0.76, CCCex = 0.86–0.87, Q2-F3 = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.
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Affiliation(s)
- Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
- Correspondence: (M.E.A.Z.); (V.H.M.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India
- Correspondence: (M.E.A.Z.); (V.H.M.)
| | | | - Israa Lewaa
- Department of Business Administration, Faculty of Business Administration, Economics and Political Science, British University in Egypt, Cairo 11837, Egypt;
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Taraji M, Haddad PR. Method Optimisation in Hydrophilic-Interaction Liquid Chromatography by Design of Experiments Combined with Quantitative Structure–Retention Relationships. Aust J Chem 2021. [DOI: 10.1071/ch21102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (tR) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R2 of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.
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Masand VH, Akasapu S, Gandhi A, Rastija V, Patil MK. Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2020; 206:104172. [PMID: 33518858 PMCID: PMC7833253 DOI: 10.1016/j.chemolab.2020.104172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 09/09/2020] [Accepted: 10/02/2020] [Indexed: 05/12/2023]
Abstract
In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80-0.82, Q2 loo = 0.74-0.77, Q 2 LMO = 0.66-0.67). The developed QSAR models identified number of sp2 hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.
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Key Words
- ADMET, Absorption, Distribution, Metabolism, Excretion and Toxicity
- COVID-19
- GA, Genetic algorithm
- MLR, Multiple linear Regression
- OECD, Organisation for Economic Co-operation and Development
- OFS, Objective Feature Selection
- OLS, Ordinary Least Square
- Peptide-type compounds
- QSAR
- QSAR, Quantitative structure-activity analysis
- QSARINS, QSAR Insubria
- SARS-CoV
- SARS-CoV-2
- SFS, Subjective Feature Selection
- SMILES, Simplified molecular-Input Line-Entry System
- WHO, World health organization
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India
| | | | - Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra, 431 004, India
| | - Vesna Rastija
- Department of Agroecology and Environmental Protection, Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
| | - Meghshyam K Patil
- Department of Chemistry, Osmanabad Sub-Centre Dr. Babasaheb Ambedkar Marathwada University, Osmanabad, Maharashtra, India
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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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Thapa B, Erickson J, Raghavachari K. Quantum Mechanical Investigation of Three-Dimensional Activity Cliffs Using the Molecules-in-Molecules Fragmentation-Based Method. J Chem Inf Model 2020; 60:2924-2938. [PMID: 32407081 DOI: 10.1021/acs.jcim.9b01123] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The concept of activity cliff (AC) (i.e., a small structural modification resulting in a substantial bioactivity change) is widely encountered in medicinal chemistry during compound design. Whereas the study of ACs is of high interest as it provides a wealth of opportunities for effective drug design, its practical application in the actual drug development process has been difficult because of significant computational challenges. To provide some understanding of the ACs, we have carried out a rigorous quantum-mechanical investigation of the electronic interactions of a wide range of ACs (205 cliffs formed by 261 protein-ligand complexes covering 37 different receptor types) using multilayer molecules-in-molecules (MIM) fragmentation-based methodology. The MIM methodology enables performing accurate high-level quantum mechanical (QM) calculations at a substantially lower computational cost, while allowing for a quantitative decomposition of the protein-ligand binding energy into the contributions from individual residues, solvation, and entropy. Our investigation in this study is mainly focused on whether the QM binding energy calculation can correctly identify the higher potency cliff partner for a given ligand pair having a sufficiently high activity difference. We have also analyzed the effect of including crystal water molecules as a part of the receptor as well as the impact of ligand desolvation energy on the correct identification of the more potent ligand in a cliff pair. Our analysis reveals that, in the majority of the cases, the AC prediction could be significantly improved by carefully identifying the critical crystal water molecules, whereas the contribution from the ligand desolvation also remains essential. Additionally, we have exploited the residue-specific interaction energies provided by MIM to identify the key residues and interaction hot-spots that are responsible for the experimentally observed drastic activity changes. The results show that our MIM fragmentation-based protocol provides comprehensive interaction energy profiles that can be employed to understand the distinctiveness of ligand modifications, for potential applications in structure-based drug design.
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Affiliation(s)
- Bishnu Thapa
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Jon Erickson
- Lilly Research Laboratories, Eli Lilly & Company, Indianapolis, Indiana 46285, United States
| | - Krishnan Raghavachari
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
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Masand VH, Elsayed NN, Thakur SD, Gawhale N, Rathore MM. Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D-QSAR Analysis. Mol Inform 2019; 38:e1800149. [PMID: 31131980 DOI: 10.1002/minf.201800149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 05/07/2019] [Indexed: 11/08/2022]
Abstract
In the present work, 2D- and 3D-quantitative structure-activity relationship (QSAR) analysis has been employed for a diverse set of eighty-nine quinoxalinones to identify the pharmacophoric features with significant correlation with the aldose reductase inhibitory activity. Using genetic algorithm (GA) as a variable selection method, multivariate linear regression (MLR) models were derived using a pool of molecular descriptors. All the six-descriptor based GA-MLR QSAR models are statistically robust with coefficient of determination (R2 )>0.80 and cross-validated R2 >0.77. The derived GA-MLR models were thoroughly validated using internal and external and Y-scrambling techniques. The CoMFA like model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, is highly robust with R2 >0.93 and cross-validated R2 >0.73. The established QSAR and CoMFA like models are proficient in identify key pharmacophoric features that govern the aldose reductase inhibitory activity of quinoxalinones.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati College, Camp, Amravati, Maharashtra, 444 602, India
| | - Nahed N Elsayed
- Department of Chemistry, College of Science, "Girls Section", King Saud University, PO Box 22452, Riyadh, 11495, Saudi Arabia.,National Organization for Drug Control and Research (NODCAR), 51 Wezaret El-Zerra St., Giza, 35521, Egypt
| | - Sumersingh D Thakur
- Department of Chemistry, RDIK and NKD College, Badnera-Amravati, Maharashtra, India
| | - Nandkishor Gawhale
- Department of Chemistry, G. S. Tompe College, Chandur Bazaar, Amravati, Maharashtra, India
| | - Mithilesh M Rathore
- Department of Chemistry, Vidya Bharati College, Camp, Amravati, Maharashtra, 444 602, India
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Masand VH, El-Sayed NN, Bambole MU, Patil VR, Thakur SD. Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2018.07.080] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Piir G, Kahn I, García-Sosa AT, Sild S, Ahte P, Maran U. Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:126001. [PMID: 30561225 PMCID: PMC6371683 DOI: 10.1289/ehp3264] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 10/19/2018] [Accepted: 11/07/2018] [Indexed: 05/31/2023]
Abstract
BACKGROUND Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented. OBJECTIVES The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs. METHODS A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots. RESULTS Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles. DISCUSSION Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.
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Affiliation(s)
- Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Iiris Kahn
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | | | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Priit Ahte
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
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Sun L, Yang H, Li J, Wang T, Li W, Liu G, Tang Y. In Silico Prediction of Compounds Binding to Human Plasma Proteins by QSAR Models. ChemMedChem 2017; 13:572-581. [DOI: 10.1002/cmdc.201700582] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 10/18/2017] [Indexed: 12/18/2022]
Affiliation(s)
- Lixia Sun
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Tianduanyi Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy; East China University of Science and Technology; Shanghai 200237 China
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Flores-Sandoval CA, Castro LV, Flores EA, Alvarez F, Garcia-Murillo A, López A, Hernández-Cortez JG, Vázquez FS. Experimental and theoretical study of bifunctionalized PEO–PPO–PEO triblock copolymers with applications as dehydrating agents for heavy crude oil. ARAB J CHEM 2017. [DOI: 10.1016/j.arabjc.2014.01.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines. J Mol Struct 2017. [DOI: 10.1016/j.molstruc.2016.11.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures. J Chromatogr A 2017; 1486:59-67. [DOI: 10.1016/j.chroma.2016.12.025] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 12/07/2016] [Accepted: 12/11/2016] [Indexed: 11/23/2022]
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Masand VH, Mahajan DT, Maldhure AK, Rastija V. Quantitative structure–activity relationships (QSARs) and pharmacophore modeling for human African trypanosomiasis (HAT) activity of pyridyl benzamides and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides. Med Chem Res 2016. [DOI: 10.1007/s00044-016-1664-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Scior T, Lozano-Aponte J, Ajmani S, Hernández-Montero E, Chávez-Silva F, Hernández-Núñez E, Moo-Puc R, Fraguela-Collar A, Navarrete-Vázquez G. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight. Curr Comput Aided Drug Des 2016; 11:21-31. [PMID: 25872791 PMCID: PMC5396257 DOI: 10.2174/1573409911666150414145937] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Revised: 02/02/2015] [Accepted: 04/03/2015] [Indexed: 12/29/2022]
Abstract
In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target.
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Affiliation(s)
- Thomas Scior
- Department of Pharmacy, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Ciudad Universitaria, Edificio 105 C/106, C.P. 72570 Puebla, PUE., Mexico.
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Nantasenamat C, Worachartcheewan A, Jamsak S, Preeyanon L, Shoombuatong W, Simeon S, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V. AutoWeka: toward an automated data mining software for QSAR and QSPR studies. Methods Mol Biol 2015; 1260:119-47. [PMID: 25502379 DOI: 10.1007/978-1-4939-2239-0_8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
UNLABELLED In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. AVAILABILITY The software is freely available at http://www.mt.mahidol.ac.th/autoweka.
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Affiliation(s)
- Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand,
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32
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Multiscale quantum chemical approaches to QSAR modeling and drug design. Drug Discov Today 2014; 19:1921-7. [DOI: 10.1016/j.drudis.2014.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 08/01/2014] [Accepted: 09/26/2014] [Indexed: 12/24/2022]
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Effect of information leakage and method of splitting (rational and random) on external predictive ability and behavior of different statistical parameters of QSAR model. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1193-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Paixão P, Aniceto N, Gouveia LF, Morais JAG. Prediction of Drug Distribution in Rat and Humans Using an Artificial Neural Networks Ensemble and a PBPK Model. Pharm Res 2014; 31:3313-22. [DOI: 10.1007/s11095-014-1421-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 05/12/2014] [Indexed: 10/25/2022]
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Correa-Basurto J, Bello M, Rosales-Hernández M, Hernández-Rodríguez M, Nicolás-Vázquez I, Rojo-Domínguez A, Trujillo-Ferrara J, Miranda R, Flores-Sandoval C. QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites. Chem Biol Interact 2014; 209:1-13. [DOI: 10.1016/j.cbi.2013.12.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Revised: 11/25/2013] [Accepted: 12/02/2013] [Indexed: 11/30/2022]
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Cumming JG, Davis AM, Muresan S, Haeberlein M, Chen H. Chemical predictive modelling to improve compound quality. Nat Rev Drug Discov 2014; 12:948-62. [PMID: 24287782 DOI: 10.1038/nrd4128] [Citation(s) in RCA: 156] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.
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Affiliation(s)
- John G Cumming
- Chemistry Innovation Centre, Discovery Sciences, AstraZeneca R&D, Alderley Park, Macclesfield SK10 4TG, UK
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Masand VH, Mahajan DT, Ben Hadda T, Jawarkar RD, Alafeefy AM, Rastija V, Ali MA. Does tautomerism influence the outcome of QSAR modeling? Med Chem Res 2013. [DOI: 10.1007/s00044-013-0776-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Enhanced QSAR model performance by integrating structural and gene expression information. Molecules 2013; 18:10789-801. [PMID: 24008242 PMCID: PMC6270197 DOI: 10.3390/molecules180910789] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 07/20/2013] [Accepted: 07/26/2013] [Indexed: 11/29/2022] Open
Abstract
Despite decades of intensive research and a number of demonstrable successes, quantitative structure-activity relationship (QSAR) models still fail to yield predictions with reasonable accuracy in some circumstances, especially when the QSAR paradox occurs. In this study, to avoid the QSAR paradox, we proposed a novel integrated approach to improve the model performance through using both structural and biological information from compounds. As a proof-of-concept, the integrated models were built on a toxicological dataset to predict non-genotoxic carcinogenicity of compounds, using not only the conventional molecular descriptors but also expression profiles of significant genes selected from microarray data. For test set data, our results demonstrated that the prediction accuracy of QSAR model was dramatically increased from 0.57 to 0.67 with incorporation of expression data of just one selected signature gene. Our successful integration of biological information into classic QSAR model provided a new insight and methodology for building predictive models especially when QSAR paradox occurred.
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Hardebeck LKE, Johnson CA, Hudson GA, Ren Y, Watt M, Kirkpatrick CC, Znosko BM, Lewis M. Predicting DNA-intercalator binding: the development of an arene-arene stacking parameter from SAPT analysis of benzene-substituted benzene complexes. J PHYS ORG CHEM 2013. [DOI: 10.1002/poc.3184] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Laura K. E. Hardebeck
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Charles A. Johnson
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Graham A. Hudson
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Yi Ren
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Michelle Watt
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Charles C. Kirkpatrick
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Brent M. Znosko
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
| | - Michael Lewis
- Department of Chemistry; Saint Louis University; 3501 Laclede Avenue Saint Louis MO 63103 USA
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Jing T, Feng J, Li D, Liu J, He G. Rational Design of Angiotensin-I-Converting Enzyme Inhibitory Peptides by Integrating in silico Modeling and an in vitro Assay. ChemMedChem 2013; 8:1057-66. [DOI: 10.1002/cmdc.201300132] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2013] [Revised: 04/27/2013] [Indexed: 12/31/2022]
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Payne MP, Button WG. Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:439-460. [PMID: 23600431 DOI: 10.1080/1062936x.2013.783507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.
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Gori GB. Health and environment regulation: ethical standards of evidence. Regul Toxicol Pharmacol 2013; 66:163-5. [PMID: 23314067 DOI: 10.1016/j.yrtph.2013.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The regulation of health and environmental hazards is coercive to the point of imposing substantial fines and even detention to transgressors. In free societies, those regulations have ethical standing if grounded on the scientific evidence of physical measurements relevant to what is being protected including humans, or on transparent use and benefit tradeoffs. These requirements are commonly observed for risks and hazards that are present and measurable, and they sustain much of current regulations that remain uncontroversial. By contrast, most supposable distant risks that cannot be measured are currently regulated on the basis of arbitrary and very costly assumptions of evidence, simulated as if relevant, objective and scientific. Such illusory assumptions are enforced to impart an ersatz moral authority to regulations, which in reality end up as the opaque condensations of political and bureaucratic pressures. The common outcome of such pressures is regulations that whimsically sanction the most minimal exposures that still allow practical applications. Such official misrepresentations of evidence are intolerable and should be removed, because only relevant and factually measured evidence provides moral standing to regulations. When that evidence is not possible, precautionary minimal exposures compatible with necessary applications should be ethically defined by reasoned and transparent use and benefits tradeoffs.
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Reliably assessing prediction reliability for high dimensional QSAR data. Mol Divers 2012; 17:63-73. [DOI: 10.1007/s11030-012-9415-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Accepted: 12/03/2012] [Indexed: 10/27/2022]
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Zakharov AV, Peach ML, Sitzmann M, Filippov IV, McCartney HJ, Smith LH, Pugliese A, Nicklaus MC. Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med Chem 2012; 4:1933-44. [PMID: 23088274 PMCID: PMC4117347 DOI: 10.4155/fmc.12.152] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes. METHOD A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans. CONCLUSION In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server ( http://cactus.nci.nih.gov ).
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Affiliation(s)
- Alexey V Zakharov
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Megan L Peach
- Basic Science Program, SAICF-rederick, SAIC, Inc., CADD Group, Chemical Biology Laboratory, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Markus Sitzmann
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Igor V Filippov
- Basic Science Program, SAICF-rederick, SAIC, Inc., CADD Group, Chemical Biology Laboratory, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
| | - Heather J McCartney
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
- Interdisciplinary Graduate Program, Biomedical Sciences, Vanderbilt University, Nashville, TN 37240, USA
| | - Layton H Smith
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Medical Research Institute, 6400 Sanger Road, Orlando, FL 32827, USA
| | - Angelo Pugliese
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
- Computer-Aided Drug Design at Cancer Research UK, Beatson Laboratories, Drug Discovery Programme, Switchback Road, Bearsden, Glasgow, G61 1BD, UK
| | - Marc C Nicklaus
- CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, Frederick National Laboratory for Cancer Research, Building 376, 376 Boyles Street, Frederick, MD 21702, USA
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45
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Pérez-Castillo Y, Lazar C, Taminau J, Froeyen M, Cabrera-Pérez MÁ, Nowé A. GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design. J Chem Inf Model 2012; 52:2366-86. [DOI: 10.1021/ci300146h] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Yunierkis Pérez-Castillo
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
- Molecular Simulations and Drug
Design Group, Centro de Bioactivos Químicos, Universidad Central “Marta Abreu” de Las Villas, Santa
Clara, Cuba
- Laboratory for
Medicinal Chemistry,
Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, B-3000 Leuven, Belgium
| | - Cosmin Lazar
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
| | - Jonatan Taminau
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
| | - Mathy Froeyen
- Laboratory for
Medicinal Chemistry,
Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, B-3000 Leuven, Belgium
| | - Miguel Ángel Cabrera-Pérez
- Molecular Simulations and Drug
Design Group, Centro de Bioactivos Químicos, Universidad Central “Marta Abreu” de Las Villas, Santa
Clara, Cuba
- Engineering
Department, Pharmacy and Pharmaceutical Technology Area,
Faculty of Pharmacy, University Miguel Hernandez, Alicante 03550, Spain
| | - Ann Nowé
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
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Su BH, Tu YS, Esposito EX, Tseng YJ. Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions. J Chem Inf Model 2012; 52:1660-73. [DOI: 10.1021/ci300060b] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Bo-Han Su
- Department
of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road,
Taipei, Taiwan 106
| | - Yi-shu Tu
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4,
Roosevelt Road, Taipei, Taiwan 106
| | | | - Yufeng J. Tseng
- Department
of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road,
Taipei, Taiwan 106
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4,
Roosevelt Road, Taipei, Taiwan 106
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Niijima S, Shiraishi A, Okuno Y. Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors. J Chem Inf Model 2012; 52:901-12. [DOI: 10.1021/ci200607f] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Satoshi Niijima
- Department
of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical
Sciences, Kyoto University, Kyoto, Japan
| | - Akira Shiraishi
- Department
of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical
Sciences, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- Department
of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical
Sciences, Kyoto University, Kyoto, Japan
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48
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Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H. Comparative Studies on Some Metrics for External Validation of QSPR Models. J Chem Inf Model 2012; 52:396-408. [DOI: 10.1021/ci200520g] [Citation(s) in RCA: 350] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Indrani Mitra
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Supratik Kar
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Rudra Narayan Das
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Humayun Kabir
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
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49
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Song IS, Cha JY, Lee SK. Prediction and analysis of acute fish toxicity of pesticides to the rainbow trout using 2D-QSAR. ANALYTICAL SCIENCE AND TECHNOLOGY 2011. [DOI: 10.5806/ast.2011.24.6.544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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