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Raju B, Narendra G, Verma H, Kumar M, Sapra B, Kaur G, jain SK, Silakari O. Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors. ACS OMEGA 2022; 7:31999-32013. [PMID: 36120033 PMCID: PMC9476183 DOI: 10.1021/acsomega.2c02983] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07-3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.
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Affiliation(s)
- Baddipadige Raju
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gera Narendra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Himanshu Verma
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Manoj Kumar
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Bharti Sapra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gurleen Kaur
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Subheet Kumar jain
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Om Silakari
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
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Li R, Tian Y, Yang Z, Ji Y, Ding J, Yan A. Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods. Mol Divers 2022:10.1007/s11030-022-10466-w. [PMID: 35737257 DOI: 10.1007/s11030-022-10466-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/19/2022] [Indexed: 10/17/2022]
Abstract
Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a training set and a test set using two splitting methods: (1) Kohonen's self-organizing map and (2) random splitting. The molecular structures were represented by MACCS fingerprints, RDKit fingerprints, topological torsions fingerprints and ECFP4 fingerprints. A total of 80 classification models were built by using five machine learning methods, including decision tree (DT), random forest, support vector machine, eXtreme Gradient Boosting and deep neural network. Model 15A_2 built by the XGBoost algorithm based on ECFP4 fingerprints showed the best performance, with an accuracy of 88.08% and an MCC value of 0.76 on the test set. Finally, we clustered the 7313 HDAC1 inhibitors into 31 subsets, and the substructural features in each subset were investigated. Moreover, using DT algorithm we analyzed the structure-activity relationship of HDAC1 inhibitors. It may conclude that some substructures have a significant effect on high activity, such as N-(2-amino-phenyl)-benzamide, benzimidazole, AR-42 analogues, hydroxamic acid with a middle chain alkyl and 4-aryl imidazole with a midchain of alkyl whose α carbon is chiral.
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Affiliation(s)
- Rourou Li
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Yujia Tian
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Zhenwu Yang
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Yueshan Ji
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Jiaqi Ding
- School of International Education, Beijing University of Chemical Technology, Beijing, China
| | - Aixia Yan
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China.
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Ji Y, Li R, Tian Y, Chen G, Yan A. Classification models and SAR analysis on thromboxane A 2 synthase inhibitors by machine learning methods. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:429-462. [PMID: 35678125 DOI: 10.1080/1062936x.2022.2078880] [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: 01/25/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Thromboxane A2 synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (dSTD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, dSTD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.
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Affiliation(s)
- Y Ji
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - R Li
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - Y Tian
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - G Chen
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - A Yan
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
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Takács G, Sándor M, Szalai Z, Kiss R, Balogh GT. Analysis of the uncharted, druglike property space by self-organizing maps. Mol Divers 2021; 26:2427-2441. [PMID: 34709525 PMCID: PMC9532340 DOI: 10.1007/s11030-021-10343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/15/2021] [Indexed: 12/29/2022]
Abstract
Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets.
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Affiliation(s)
- Gergely Takács
- Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Műegyetem rakpart 3, Budapest, 1111, Hungary
- Mcule.com Kft, Bartók Béla út 105-113, Budapest, 1115, Hungary
| | - Márk Sándor
- Mcule.com Kft, Bartók Béla út 105-113, Budapest, 1115, Hungary
| | - Zoltán Szalai
- Mcule.com Kft, Bartók Béla út 105-113, Budapest, 1115, Hungary
| | - Róbert Kiss
- Mcule.com Kft, Bartók Béla út 105-113, Budapest, 1115, Hungary.
| | - György T Balogh
- Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Műegyetem rakpart 3, Budapest, 1111, Hungary.
- Department of Pharmacodynamics and Biopharmacy, University of Szeged, Szeged, 6720, Hungary.
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Abstract
INTRODUCTION Indolizines are structural isomers with indoles. Although several indole-based commercial drugs are available in the market, none of the indolizine-based drugs are available up-to-date. Natural and synthetic indolizines have a wide-range of pharmaceutical importance such as antitumor, antimycobacterial, antagonist, and antiproliferative activities. This prompted us to search and collect all possible data about the pharmacological importance of indolizine to open an avenue to the researchers in exploring more medicinal applications of such biologically important compounds. AREAS COVERED The current review article covers the advancements in the biological and pharmacological activities of indolizine-based compounds during the last decade. The covered areas of this work involved anticancer, anti-HIV-1, anti-inflammatory, antimicrobial, anti-tubercular, larvicidal, anti-schizophrenia, CRTh2 antagonist's activities in addition to enzymatic inhibitory activity. EXPERT OPINION The discovery of indolizine drugs will be a major breakthrough as compared with their widely available drug-containing indole isosteres. Major work collected here was focused on anticancer, anti-tubercular, anti-inflammatory, and enzymatic inhibitory activities. The SAR study of the reported biologically active indolizines is summarized throughout the review whenever highlighted to the rationale the behavior of inhibitory action. Several indolizines with certain functions provided great enhancement in the therapeutic activities comparing with reference drugs.
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Affiliation(s)
- Kamal M Dawood
- Department of Chemistry, Faculty of Science, Cairo University , Giza, Egypt
| | - Ashraf A Abbas
- Department of Chemistry, Faculty of Science, Cairo University , Giza, Egypt
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