Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Rajput, Abhishek; Kumar, Manoj

doi:10.1007/s11030-021-10291-7

Cited by 7 publications

(12 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the biological activity of the shortlisted compounds was predicted using a Bayesian algorithm [ 90 , 91 , 92 , 93 ]. The anti-EBOV inhibition efficiencies of the compounds were also predicted using RF and SVM models [ 94 ]. Structural similarity searches of the shortlisted compounds were also performed to identify compounds with known antiviral or anti-EBOV related activity.…”

Section: Resultsmentioning

confidence: 99%

“…The anti-EBOV inhibition efficiencies of the shortlisted compounds were predicted via RF and SVM models using Anti-Ebola [ 94 ]. Anti-Ebola is a regression-based prediction algorithm that predicts the potential EBOV-inhibitory activity of a query compound using quantitative structure–activity relationship (QSAR) analysis [ 94 ]. These models were previously validated using compounds that have been experimentally shown to possess anti-EBOV activity [ 94 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

Broni

Ashley

Adams

et al. 2023

IJMS

View full text Add to dashboard Cite

The Ebola virus (EBOV) is still highly infectious and causes severe hemorrhagic fevers in primates. However, there are no regulatorily approved drugs against the Ebola virus disease (EVD). The highly virulent and lethal nature of EVD highlights the need to develop therapeutic agents. Viral protein 40 kDa (VP40), the most abundantly expressed protein during infection, coordinates the assembly, budding, and release of viral particles into the host cell. It also regulates viral transcription and RNA replication. This study sought to identify small molecules that could potentially inhibit the VP40 protein by targeting the N-terminal domain using an in silico approach. The statistical quality of AutoDock Vina’s capacity to discriminate between inhibitors and decoys was determined, and an area under the curve of the receiver operating characteristic (AUC-ROC) curve of 0.791 was obtained. A total of 29,519 natural-product-derived compounds from Chinese and African sources as well as 2738 approved drugs were successfully screened against VP40. Using a threshold of −8 kcal/mol, a total of 7, 11, 163, and 30 compounds from the AfroDb, Northern African Natural Products Database (NANPDB), traditional Chinese medicine (TCM), and approved drugs libraries, respectively, were obtained after molecular docking. A biological activity prediction of the lead compounds suggested their potential antiviral properties. In addition, random-forest- and support-vector-machine-based algorithms predicted the compounds to be anti-Ebola with IC50 values in the micromolar range (less than 25 μM). A total of 42 natural-product-derived compounds were identified as potential EBOV inhibitors with desirable ADMET profiles, comprising 1, 2, and 39 compounds from NANPDB (2-hydroxyseneganolide), AfroDb (ZINC000034518176 and ZINC000095485942), and TCM, respectively. A total of 23 approved drugs, including doramectin, glecaprevir, velpatasvir, ledipasvir, avermectin B1, nafarelin acetate, danoprevir, eltrombopag, lanatoside C, and glycyrrhizin, among others, were also predicted to have potential anti-EBOV activity and can be further explored so that they may be repurposed for EVD treatment. Molecular dynamics simulations coupled with molecular mechanics Poisson–Boltzmann surface area calculations corroborated the stability and good binding affinities of the complexes (−46.97 to −118.9 kJ/mol). The potential lead compounds may have the potential to be developed as anti-EBOV drugs after experimental testing.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

Broni

Ashley

Adams

et al. 2023

IJMS

View full text Add to dashboard Cite

show abstract

“…Furthermore, the compounds selected in this study were compounds that possessed probable activity (Pa) values greater than their probable inactivity (Pi) [ 107 ], reinforcing the need for the in vitro testing of their anti-EBOV activity [ 108 ]. Additionally, the predicted anti-EBOV inhibition efficiency values (IC 50 ), using a random forest-based classifier [ 60 ] for NANPDB2412, NANPDB2476, NANPDB4048 and ZINC000095486250 were obtained as 11.48, 8.83, 3.35 and 11.99 μM, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The Prediction of Activity Spectra for Substances (PASS) tool was used to characterize the biological activity of the compounds using their structures in the SMILES file format [ 58 , 59 ]. The anti-EBOV inhibition efficiency was predicted using the SDF files of the compounds via a random forest-based model [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

et al. 2021

View full text Add to dashboard Cite

Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC50), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

show abstract

“…A more recent application of ML in EBOV drug discovery is the work by Rajput and Kumar (2022) [ 45 ] in which SVM, random forest, and artificial neural networks were employed using tenfold cross-validation ( Table 1 ). In their report, the best predictive model showed a Pearson’s correlation coefficient ranging from 0.83 to 0.98 on training/testing (T274) dataset.…”

Section: Machine Learning Algorithms Deployed In Ebola Virus Drug Dis...mentioning

confidence: 99%

Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery

et al. 2023

View full text Add to dashboard Cite

The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline.

show abstract

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Cited by 7 publications

References 40 publications

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery

Contact Info

Product

Resources

About