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Ncube NB, Tukulula M, Govender KG. Leveraging computational tools to combat malaria: assessment and development of new therapeutics. J Cheminform 2024; 16:50. [PMID: 38698437 PMCID: PMC11064327 DOI: 10.1186/s13321-024-00842-z] [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: 01/10/2024] [Accepted: 04/12/2024] [Indexed: 05/05/2024] Open
Abstract
As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.
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Affiliation(s)
- Nomagugu B Ncube
- School of Chemistry and Physics, College of Agriculture, Engineering and Science (CAES), University of KwaZulu-Natal, Westville Campus, Durban, 4001, South Africa
| | - Matshawandile Tukulula
- School of Chemistry and Physics, College of Agriculture, Engineering and Science (CAES), University of KwaZulu-Natal, Westville Campus, Durban, 4001, South Africa.
| | - Krishna G Govender
- Department of Chemical Sciences, University of Johannesburg, Johannesburg, 2028, South Africa.
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Evbuomwan IO, Alejolowo OO, Elebiyo TC, Nwonuma CO, Ojo OA, Edosomwan EU, Chikwendu JI, Elosiuba NV, Akulue JC, Dogunro FA, Rotimi DE, Osemwegie OO, Ojo AB, Ademowo OG, Adeyemi OS, Oluba OM. In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy. J Biomol Struct Dyn 2024; 42:101-118. [PMID: 36974933 DOI: 10.1080/07391102.2023.2192799] [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: 01/02/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023]
Abstract
The emergence of varying levels of resistance to currently available antimalarial drugs significantly threatens global health. This factor heightens the urgency to explore bioactive compounds from natural products with a view to discovering and developing newer antimalarial drugs with novel mode of actions. Therefore, we evaluated the inhibitory effects of sixteen phytocompounds from Cymbopogon citratus leaf extract against Plasmodium falciparum drug targets such as P. falciparum circumsporozoite protein (PfCSP), P. falciparum merozoite surface protein 1 (PfMSP1) and P. falciparum erythrocyte membrane protein 1 (PfEMP1). In silico approaches including molecular docking, pharmacophore modeling and 3D-QSAR were adopted to analyze the inhibitory activity of the compounds under consideration. The molecular docking results indicated that a compound swertiajaponin from C. citratus exhibited a higher binding affinity (-7.8 kcal/mol) to PfMSP1 as against the standard artesunate-amodiaquine (-6.6 kcal/mol). Swertiajaponin also formed strong hydrogen bond interactions with LYS29, CYS30, TYR34, ASN52, GLY55 and CYS28 amino acid residues. In addition, quercetin another compound from C. citratus exhibited significant binding energies -6.8 and -8.3 kcal/mol with PfCSP and PfEMP1, respectively but slightly lower than the standard artemether-lumefantrine with binding energies of -7.4 kcal/mol against PfCSP and -8.7 kcal/mol against PfEMP1. Overall, the present study provides evidence that swertiajaponin and other phytomolecules from C. citratus have modulatory properties toward P. falciparum drug targets and thus may warrant further exploration in early drug discovery efforts against malaria. Furthermore, these findings lend credence to the folkloric use of C. citratus for malaria treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ikponmwosa Owen Evbuomwan
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Department of Food Science and Microbiology, Landmark University, Omu-Aran, Nigeria
| | - Omokolade Oluwaseyi Alejolowo
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | - Charles Obiora Nwonuma
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | - Oluwafemi Adeleke Ojo
- Phytomedicine, Molecular Toxicology and Computational Biochemistry Research Group, Department of Biochemistry, Bowen University, Iwo, Nigeria
| | - Evelyn Uwa Edosomwan
- Department of Animal and Environmental Biology, University of Benin, Benin City, Nigeria
| | | | | | | | | | - Damilare Emmanuel Rotimi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | | | - Olusegun George Ademowo
- Department of Pharmacology and Therapeutics, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Nigeria
- Drug Research Laboratory, Institute of Advanced Medical Research and Training (IMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oluyomi Stephen Adeyemi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Laboratory of Sustainable Animal Environment, Graduate School of Agricultural Science, Tohoku University, Osaki, Miyagi, Japan
| | - Olarewaju Michael Oluba
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
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3
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Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions. J CHEM-NY 2022. [DOI: 10.1155/2022/4148443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes.
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Oguike OE, Ugwuishiwu CH, Asogwa CN, Nnadi CO, Obonga WO, Attama AA. Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum. Mol Divers 2022; 26:3447-3462. [PMID: 35064444 PMCID: PMC8782692 DOI: 10.1007/s11030-022-10380-1] [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: 10/05/2021] [Accepted: 01/07/2022] [Indexed: 11/29/2022]
Abstract
Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC50 values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.
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Affiliation(s)
- Osondu Everestus Oguike
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Chikodili Helen Ugwuishiwu
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Caroline Ngozi Asogwa
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Computer Science, Faculty of Physical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Charles Okeke Nnadi
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria. .,Deprtment of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.
| | - Wilfred Ofem Obonga
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Deprtment of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
| | - Anthony Amaechi Attama
- Machine Learning Research Group, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.,Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria
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Nguyen PTV, Van Dat T, Mizukami S, Nguyen DLH, Mosaddeque F, Kim SN, Nguyen DHB, Đinh OT, Vo TL, Nguyen GLT, Quoc Duong C, Mizuta S, Tam DNH, Truong MP, Huy NT, Hirayama K. 2D-quantitative structure-activity relationships model using PLS method for anti-malarial activities of anti-haemozoin compounds. Malar J 2021; 20:264. [PMID: 34116665 PMCID: PMC8196453 DOI: 10.1186/s12936-021-03775-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/16/2021] [Indexed: 11/18/2022] Open
Abstract
Background Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.
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Affiliation(s)
- Phuong Thuy Viet Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam.
| | - Truong Van Dat
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Shusaku Mizukami
- Department of Immunogenetics, Institute of Tropical Medicine (NEKKEN), Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan.,Leading Programme, Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan
| | - Duy Le Hoang Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Farhana Mosaddeque
- Department of Immunogenetics, Institute of Tropical Medicine (NEKKEN), Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan
| | - Son Ngoc Kim
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Duy Hoang Bao Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Oanh Thi Đinh
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Tu Linh Vo
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Giang Le Tra Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Cuong Quoc Duong
- Faculty of Pharmacy, University of Medicine and Pharmacy At Ho Chi Minh City, Ho Chi Minh City, 700000, Viet Nam
| | - Satoshi Mizuta
- Center for Bioinformatics and Molecular Medicine, Graduate School of Biomedical Sciences, Nagasaki University, 1-14 Bunkyo, Nagasaki, Japan
| | - Dao Ngoc Hien Tam
- Asia Shine Trading & Service Co. Ltd., Ho Chi Minh City, 70000, Vietnam
| | - M Phuong Truong
- American University of the Carribean School of Medicine, 1 University Drive at Jordan Road, Cupecoy, Sint Maarten
| | - Nguyen Tien Huy
- School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan.
| | - Kenji Hirayama
- Department of Immunogenetics, Institute of Tropical Medicine (NEKKEN), Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan.
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Ibrahim ZY, Uzairu A, Shallangwa G, Abechi S. In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum. CHEMISTRY AFRICA 2020. [DOI: 10.1007/s42250-020-00199-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
AbstractA blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).
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Abstract
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.
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Understanding the potency of malarial ligand (D44) in plasmodium FKBP35 and modelled halogen atom (Br, Cl, F) functional groups. J Mol Graph Model 2020; 97:107553. [PMID: 32035313 DOI: 10.1016/j.jmgm.2020.107553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/10/2020] [Accepted: 01/27/2020] [Indexed: 11/21/2022]
Abstract
The present study clearly depicts the understanding of the D44 in Plasmodium FKBP35 around the hinge region. To analyse the binding stability of D44 ligand and to understand the role of halogen bond, hydrogen bond interaction formed between the hinge region amino acids: Isoleucine (Ile74), Phenylalanine (Phe54), Aspartic acid (Asp55) Phenylalanine (Phe64),Tyrosine (Tyr100), Tryptophan (TRP 77) and ligand D44 was portrayed specifically through interaction energy calculations at HF, M062X, MP2 level of theories for different basis set (6-311G**, 6-31+G*, LANL2DZ). The investigation will provide an apparent picture regarding the non-covalent interaction that hold the contact of ligand and amino acids in the hinge region and the implication of modelled functional groups (Br, Cl, F, OSO and NH2) on ligand, which will help chemist in synthesizing new novel ligands. HOMO, LUMO chart calculated for D44 ligands reveals graphic illustration of orbital's that stimulate for contact. The aim and natural bond orbital analysis identified key contribution of individual hydrogen/halogen bonds that contribute for the binding strength through stabilization energy, ρ and ∇2ρ values. Overall this study finds out that the Stability of D44 in Plasmodium FKBP35 was enhanced by the Halogen atom (Br, Cl, F) functional groups; which provide an innovative pathway for the selection of functional groups that opt for the hinge region side chains on the ligand.
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