1
|
Ding H, Xing F, Zou L, Zhao L. QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking. BMC Chem 2024; 18:59. [PMID: 38555462 PMCID: PMC10981835 DOI: 10.1186/s13065-024-01165-8] [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: 05/22/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.
Collapse
Affiliation(s)
- Hao Ding
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Fei Xing
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Lin Zou
- Medical College of Guangxi University, Nanning, 530004, Guangxi, China
| | - Liang Zhao
- Hepatobiliary and Splenic Surgery Ward, Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.
| |
Collapse
|
2
|
Pandey V. Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach. Chem Biodivers 2024; 21:e202301213. [PMID: 38109053 DOI: 10.1002/cbdv.202301213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023]
Abstract
Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.
Collapse
Affiliation(s)
- Vandana Pandey
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| |
Collapse
|
3
|
Paul S, Majumdar M. Exploring antidiabetic potential of a polyherbal formulation Madhurakshak Activ: An in vitro and in silico study. Fitoterapia 2023; 169:105598. [PMID: 37380135 DOI: 10.1016/j.fitote.2023.105598] [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: 04/02/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 06/30/2023]
Abstract
Madhurakshak Activ (MA), a commercial polyherbal antidiabetic preparation is known to manage diabetes mellitus (DM) by reducing blood glucose levels. However, lacks systematic mechanistic evaluation for their molecular and cellular mode of actions. In the present study, hydro-alcoholic and aqueous extract of MA were evaluated for their effects on glucose adsorption, diffusion, amylolysis kinetics and transport across the yeast cells using in vitro techniques. Bioactive compounds identified from MA by LC-MS/MS were assessed for their binding potential against DPP-IV and PPARγ via an in silico approach. Our results revealed that the adsorption of glucose increased dose dependently (5 mM -100 mM). Both extracts exhibited linear glucose uptake into the yeast cells (5 mM - 25 mM), whereas glucose diffusion was directly proportional to time (30-180 min). Pharmacokinetic analysis revealed drug-like properties and low toxicity levels for all the selected compounds. Among the tested compounds, 6-hydroxyluteolin (-8.9 against DPP-IV and PPARγ) and glycyrrhetaldehyde (DPP-IV -9.7 and PPARγ -8.5) have exhibited higher binding affinity compared to the positive control. Therefore, the above compounds were further considered for molecular dynamics simulation which showed stability of the docked complexes. Hence, studied mode of actions might produce a concerted role of MA in increasing the rate of glucose absorption and uptake followed by the in silico studies which suggest that the compounds identified from MA may inhibit DPP-IV and PPARγ phosphorylation.
Collapse
Affiliation(s)
- Saptadipa Paul
- School of Science, JAIN (Deemed to be) University, #34, 1st Cross, J C Road, Bangalore 560027, India.
| | - Mala Majumdar
- School of Science, JAIN (Deemed to be) University, #34, 1st Cross, J C Road, Bangalore 560027, India.
| |
Collapse
|
4
|
Niazi SK, Mariam Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int J Mol Sci 2023; 24:11488. [PMID: 37511247 PMCID: PMC10380192 DOI: 10.3390/ijms241411488] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/30/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.
Collapse
Affiliation(s)
- Sarfaraz K Niazi
- College of Pharmacy, University of Illinois, Chicago, IL 61820, USA
| | - Zamara Mariam
- Zamara Mariam, School of Interdisciplinary Engineering & Sciences (SINES), National University of Sciences & Technology (NUST), Islamabad 24090, Pakistan
| |
Collapse
|
5
|
Damavandi S, Shiri F, Emamjomeh A, Pirhadi S, Beyzaei H. A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling. BMC Chem 2023; 17:70. [PMID: 37415191 DOI: 10.1186/s13065-023-00991-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023] Open
Abstract
Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.
Collapse
Affiliation(s)
- Sedigheh Damavandi
- Department of Bioinformatics, Laboratory of Computational Biotechnology and Bioinformatics (CBB Lab), University of Zabol, Zabol, Iran
| | - Fereshteh Shiri
- Department of Chemistry, Faculty of Science, University of Zabol, Zabol, Iran.
| | - Abbasali Emamjomeh
- Department of Bioinformatics, Laboratory of Computational Biotechnology and Bioinformatics (CBB Lab), University of Zabol, Zabol, Iran
- Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Beyzaei
- Department of Chemistry, Faculty of Science, University of Zabol, Zabol, Iran
| |
Collapse
|
6
|
Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to Vibrio fischeri. Molecules 2023; 28:molecules28062703. [PMID: 36985675 PMCID: PMC10057455 DOI: 10.3390/molecules28062703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
Vibrio fischeri is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class − 1 with log1/IBC50 ≤ 4.2 and Class + 1 with log1/IBC50 > 4.2, the unit of IBC50: mol/L) by utilizing a large data set of 601 toxicity log1/IBC50 of organic compounds to Vibrio fischeri. Dragon software was used to calculate 4885 molecular descriptors for each compound. Stepwise multiple linear regression (MLR) analysis was used to select the descriptor subset for the models. The ten molecular descriptors used in the classification model reflect the structural information on the Michael-type addition of nucleophiles, molecular branching, molecular size, polarizability, hydrophobic, and so on. Furthermore, these descriptors were interpreted from the point of view of toxicity mechanisms. The optimal support vector machine (SVM) model (C = 253.8 and γ = 0.009) was obtained with the genetic algorithm. The SVM classification model produced a prediction accuracy of 89.1% for the training set (451 log1/IBC50), of 80.0% for the test set (150 log1/IBC50), and of 86.9% for the total data set (601 log1/IBC50), which are higher than that (80.5%, 76%, and 79.4%, respectively) from the binary logistic regression (BLR) model. The global SVM classification model is successful, although it deals with a large data set in relation to the toxicity of organics to Vibrio fischeri.
Collapse
|
7
|
Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci 2023; 181:106324. [PMID: 36347444 DOI: 10.1016/j.ejps.2022.106324] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022]
Abstract
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.
Collapse
Affiliation(s)
- Divya Vemula
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Perka Jayasurya
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Varthiya Sushmitha
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | | | - Vasundhra Bhandari
- National Institute of Pharmaceutical Education and Research- Hyderabad, India.
| |
Collapse
|
8
|
Khan HA, Jabeen I. Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors. Front Pharmacol 2022; 13:825741. [PMID: 35300294 PMCID: PMC8921698 DOI: 10.3389/fphar.2022.825741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/03/2022] [Indexed: 01/31/2023] Open
Abstract
Leukotrienes (LTs) are pro-inflammatory lipid mediators derived from arachidonic acid (AA), and their high production has been reported in multiple allergic, autoimmune, and cardiovascular disorders. The biological synthesis of leukotrienes is instigated by transfer of AA to 5-lipoxygenase (5-LO) via the 5-lipoxygenase-activating protein (FLAP). Suppression of FLAP can inhibit LT production at the earliest level, providing relief to patients requiring anti-leukotriene therapy. Over the last 3 decades, several FLAP modulators have been synthesized and pharmacologically tested, but none of them could be able to reach the market. Therefore, it is highly desirable to unveil the structural requirement of FLAP modulators. Here, in this study, supervised machine learning techniques and molecular modeling strategies are adapted to vaticinate the important 2D and 3D anti-inflammatory properties of structurally diverse FLAP inhibitors, respectively. For this purpose, multiple machine learning classification models have been developed to reveal the most relevant 2D features. Furthermore, to probe the 3D molecular basis of interaction of diverse anti-inflammatory compounds with FLAP, molecular docking studies were executed. By using the most probable binding poses from docking studies, the GRIND model was developed, which indicated the positive contribution of four hydrophobic, two hydrogen bond acceptor, and two shape-based features at certain distances from each other towards the inhibitory potency of FLAP modulators. Collectively, this study sheds light on important two-dimensional and three-dimensional structural requirements of FLAP modulators that can potentially guide the development of more potent chemotypes for the treatment of inflammatory disorders.
Collapse
Affiliation(s)
- Hafiza Aliza Khan
- Research Centre for Modelling and Simulation (RCMS), NUST Interdisciplinary Cluster for Higher Education (NICHE), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Ishrat Jabeen
- Research Centre for Modelling and Simulation (RCMS), NUST Interdisciplinary Cluster for Higher Education (NICHE), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| |
Collapse
|
9
|
Aćimović MG, Cvetković MT, Stanković Jeremić JM, Pezo LL, Varga AO, Čabarkapa IS, Kiprovski B. Biological activity and profiling of
Salvia sclarea
essential oil obtained by steam and hydrodistillation extraction methods via chemometrics tools. FLAVOUR FRAG J 2021. [DOI: 10.1002/ffj.3684] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
| | - Mirjana T. Cvetković
- Department of Chemistry Institute of Chemistry, Technology and Metallurgy University of Belgrade Belgrade Serbia
| | - Jovana M. Stanković Jeremić
- Department of Chemistry Institute of Chemistry, Technology and Metallurgy University of Belgrade Belgrade Serbia
| | - Lato L. Pezo
- Institute of General and Physical Chemistry University of Belgrade Belgrade Serbia
| | - Ana O. Varga
- Institute of Food Technology University of Novi Sad Novi Sad Serbia
| | | | | |
Collapse
|
10
|
Nekoei M, Mohammadhosseini M, Pourbasheer E. A quantitative structure–activity relationship study on
CXL017
derivatives as effective drugs for cancer treatment. J CHIN CHEM SOC-TAIP 2021. [DOI: 10.1002/jccs.202100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Mehdi Nekoei
- Department of Chemistry, Faculty of Basic Sciences, Shahrood Branch Islamic Azad University Shahrood Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, Faculty of Basic Sciences, Shahrood Branch Islamic Azad University Shahrood Iran
| | - Eslam Pourbasheer
- Department of Chemistry, Faculty of Science University of Mohaghegh Ardabili Ardabil Iran
| |
Collapse
|
11
|
Abstract
This paper is a study of the chemical composition of Hyssopus officinalis ssp. officinalis grown during three years (2017–2019) at the Institute of Field and Vegetable Crops Novi Sad (Vojvodina Province, Serbia). Furthermore, comparisons with ISO standards during the years were also investigated, as well as a prediction model of retention indices of compounds from the essential oils. An essential oil obtained by hydrodistillation and analysed by GC-FID and GC-MS was isopinocamphone chemotype. The gathered information about the volatile compounds from H. officinalis was used to classify the samples using the unrooted cluster tree. The correlation analysis was applied to investigate the similarity of different samples, according to GC-MS data. The quantitative structure–retention relationship (QSRR) was also employed to predict the retention indices of the identified compounds. A total of 74 experimentally obtained retention indices were used to build a prediction model. The coefficient of determination for the training cycle was 0.910, indicating that this model could be used for the prediction of retention indices for H. officinalis essential oil compounds.
Collapse
|
12
|
Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, Speck-Planche A, Singh SK. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Curr Drug Targets 2021; 22:631-655. [PMID: 33397265 DOI: 10.2174/1389450122999210104205732] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022]
Abstract
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
Collapse
Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Poonam Tanwar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Garima Thakur
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. 8940577, San Joaquin, Santiago, Chile
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
| |
Collapse
|
13
|
Chemical Characterization of Marrubium vulgare Volatiles from Serbia. PLANTS 2021; 10:plants10030600. [PMID: 33806732 PMCID: PMC8004588 DOI: 10.3390/plants10030600] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/10/2021] [Accepted: 03/16/2021] [Indexed: 12/20/2022]
Abstract
Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated years. This variation in volatiles composition could be a consequence of weather conditions, as in the case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles from literature and average values from this study, it could be said that there are several chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene + caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be consequence of adaptation to grow in different environment, especially ecological conditions such as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes. In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile compounds was used to build the prediction models. The coefficients of determination were 0.956 and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction error and high r2.
Collapse
|
14
|
Vahedi N, Mohammadhosseini M, Nekoei M. QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches. CURR ANAL CHEM 2020. [DOI: 10.2174/1573411016999200518083359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily
present in eukaryotes.
Methods:
In the present report, some efficient linear and non-linear methods including multiple linear
regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully
used to develop and establish quantitative structure-activity relationship (QSAR) models
capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP
inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set
and selection of the training and test sets. A genetic algorithm (GA) variable selection method was
employed to select the optimal subset of descriptors that have the most significant contributions to
the overall inhibitory activity from the large pool of calculated descriptors.
Results:
The accuracy and predictability of the proposed models were further confirmed using crossvalidation,
validation through an external test set and Y-randomization (chance correlations) approaches.
Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed
models. The results revealed that non-linear modeling approaches, including SVM and ANN
could provide much more prediction capabilities.
Conclusion:
Among the constructed models and in terms of root mean square error of predictions
(RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for
the training set, the predictive power of the GA-SVM approach was better. However, compared with
MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.
Collapse
Affiliation(s)
- Nafiseh Vahedi
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Mehdi Nekoei
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| |
Collapse
|
15
|
Liao S, Yu X, Chen J, Huang X. Prediction of the half-lives of polychlorinated biphenyls based on the IEF-PCM calculations. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2019. [DOI: 10.1142/s0219633619500330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Three-dimensional structures of 62 polychlorinated biphenyl (PCB) congeners were optimized with the integral equation formalism polarizable continuum model (IEF-PCM) in combination with the density functional theory (DFT) method at 6-31G(d) level. By applying support vector machine (SVM) algorithm, a nonlinear quantitative structure–property relationship (QSPR) model was built to predict half-lives (log [Formula: see text]) of 62 PCBs in juvenile rainbow trout. The optimal SVM model based on the parameters [Formula: see text] of 854.721 and [Formula: see text] of 0.0565 produces the root-mean-square (rms) errors of 0.0352 for the training set and 0.0446 for the test set, which are less than that of the previous models reported. The results suggest that it is feasible to build SVM models for the half-lives of PCBs with IEF-PCM and B3LYP/6-31G(d) for deriving structural descriptors.
Collapse
Affiliation(s)
- Shiyao Liao
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, P. R. China
| | - Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, P. R. China
| | - Jianfang Chen
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, P. R. China
| | - Xianwei Huang
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, P. R. China
| |
Collapse
|
16
|
Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci 2019; 40:624-635. [PMID: 31383376 PMCID: PMC6710127 DOI: 10.1016/j.tips.2019.07.005] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 07/10/2019] [Accepted: 07/10/2019] [Indexed: 12/13/2022]
Abstract
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.
Collapse
Affiliation(s)
- Anna O Basile
- Columbia University Medical Center, New York, NY, USA
| | | | | |
Collapse
|
17
|
Maltarollo VG, Kronenberger T, Espinoza GZ, Oliveira PR, Honorio KM. Advances with support vector machines for novel drug discovery. Expert Opin Drug Discov 2018; 14:23-33. [PMID: 30488731 DOI: 10.1080/17460441.2019.1549033] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.
Collapse
Affiliation(s)
- Vinicius Gonçalves Maltarollo
- a Departamento de Produtos Farmacêuticos, Faculdade de Farmácia , Universidade Federal de Minas Gerais , Belo Horizonte , Brazil
| | - Thales Kronenberger
- b Department of Internal Medicine VIII , University Hospital of Tübingen , Tübingen , Germany
| | - Gabriel Zarzana Espinoza
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil
| | - Patricia Rufino Oliveira
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil
| | - Kathia Maria Honorio
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil.,d Centro de Ciências Naturais e Humanas , Universidade Federal do ABC , Santo André , Brazil
| |
Collapse
|
18
|
Asadollahi-Baboli M, Dehnavi S. Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors. Comput Biol Chem 2018; 76:283-292. [PMID: 30103106 DOI: 10.1016/j.compbiolchem.2018.07.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 07/18/2018] [Accepted: 07/31/2018] [Indexed: 02/07/2023]
Abstract
The α-glucosidase inhibitors are considered as important agents in drug discovery against diabetes mellitus. Molecular docking and quantitative structure-activity relationship (QSAR) were performed based on a series of tetracyclic oxindole derivatives to elucidate key structural properties affecting inhibitory activity and support the design of new α-glucosidase inhibitors. The molecular docking results demonstrate that at least two hydrogen bonds between Thr681 and Arg676 residues and the oxygen atoms in amid groups have an important role in the optimum binding of inhibitors. In addition, the sum of polar contacts of Arg699, Arg670, Glu792 and Glu301 residues with the α-glucosidase inhibitors have more than one third of total binding free energy. The docked conformations of the inhibitors with the best binding free energy were used to construct QSAR models. As a primary survey and a graphical comparing tool, the partial least squares-discriminant analysis (PLS-DA) technique was successfully employed to classify active and inactive inhibitors. The validated QSAR analysis were performed through genetic algorithm-partial least squares (GA-PLS) and support vector machine (SVM) techniques. The QSAR model reveals that important features of J3D, Mor26 u and HOMA have a high predictive capability (R2p = 0.837, Q2LOO = 0.871, R2LSO = 0.790 and r2m = 0.758) using GA-PLS/SVM strategy. Generally, the suggested QSAR analysis based on classification, docking and GA-PLS/SVM strategy may help suggest chemical scaffold to design novel oxindole derivatives as α-glucosidase inhibitors.
Collapse
Affiliation(s)
- M Asadollahi-Baboli
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol 47148-71167, Mazandaran, Iran.
| | - S Dehnavi
- Department of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol 47148-71167, Mazandaran, Iran
| |
Collapse
|
19
|
Panteleev J, Gao H, Jia L. Recent applications of machine learning in medicinal chemistry. Bioorg Med Chem Lett 2018; 28:2807-2815. [PMID: 30122222 DOI: 10.1016/j.bmcl.2018.06.046] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/24/2018] [Accepted: 06/26/2018] [Indexed: 12/20/2022]
Abstract
In recent decades, artificial intelligence and machine learning have played a significant role in increasing the efficiency of processes across a wide spectrum of industries. When it comes to the pharmaceutical and biotechnology sectors, numerous tools enabled by advancement of computer science have been developed and are now routinely utilized. However, there are many aspects of the drug discovery process, which can further benefit from refinement of computational methods and tools, as well as improvement of accessibility of these new technologies. In this review, examples of recent developments in machine learning application are described, which have the potential to impact different parts of the drug discovery and development flow scheme. Notably, new deep learning-based approaches across compound design and synthesis, prediction of binding, activity and ADMET properties, as well as applications of genetic algorithms are highlighted.
Collapse
Affiliation(s)
- Jane Panteleev
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Hua Gao
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Lei Jia
- Amgen Discovery Research, One Amgen Center Dr., Thousand Oaks, CA 91320, USA.
| |
Collapse
|
20
|
Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today 2018; 23:1538-1546. [PMID: 29750902 DOI: 10.1016/j.drudis.2018.05.010] [Citation(s) in RCA: 442] [Impact Index Per Article: 73.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 03/29/2018] [Accepted: 05/02/2018] [Indexed: 01/03/2023]
Abstract
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
Collapse
Affiliation(s)
- Yu-Chen Lo
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Stefano E Rensi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Wen Torng
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| |
Collapse
|
21
|
Abbasitabar F, Zare-Shahabadi V. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach. CHEMOSPHERE 2017; 172:249-259. [PMID: 28081509 DOI: 10.1016/j.chemosphere.2016.12.095] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2016] [Revised: 11/29/2016] [Accepted: 12/19/2016] [Indexed: 05/27/2023]
Abstract
Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for Rtraining2 and Rtest2, respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for Rtraining2 and Rtest2, respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (Rtraining2 and Rtest2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.
Collapse
Affiliation(s)
- Fatemeh Abbasitabar
- Department of Chemistry, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.
| | - Vahid Zare-Shahabadi
- Department of Chemistry, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
| |
Collapse
|
22
|
Arthur DE, Uzairu A, Mamza P, Stephen AE, Shallangwa G. Quantum modelling of the Structure-Activity and toxicity relationship studies of some potent compounds on SR leukemia cell line. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.cdc.2016.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|