Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors

Sequentially Released Liposomes Enhance Anti-Liver Cancer Efficacy of Tetrandrine and Celastrol-Loaded Coix Seed Oil

Background

A sequential release co-delivery system is an effective strategy to improve anti-cancer efficacy. Herein, multicomponent-based liposomes (TET-CTM/L) loaded with tetrandrine (TET) and celastrol (CEL)-loaded coix seed oil microemulsion (CTM) were fabricated, which showed synergistic anti-liver cancer activities. By virtue of Enhanced Permeability and Retention (EPR) effect, TET-CTM/L can achieve efficient accumulation at the tumor site. TET was released initially to repair abnormal vessels and decrease the fibroblasts, and CTM was released subsequently for eradication of tumor tissue.

Methods

TEM (transmission electron microscopy) and DLS (dynamic light scattering) were adopted to characterize the TET-CTM/L. Flow cytometry was adopted to examine the cellular uptake and cytotoxicity of HepG2 cells. The HepG2 xenograft nude mice were adopted to evaluate the anti-tumor efficacy and systemic safety of TET-CTM/L.

Results

TEM images of TET-CTM/L showed the structure of small particle size of CTM within large-size liposomes, indicating that CTM can be encapsulated in liposomes by film dispersion method. In in vitro studies, TET-CTM/L induced massive apoptosis toward HepG2 cells, indicating synergistic cytotoxicity against HepG2 cells. In in vivo studies, TET-CTM/L displayed diminished systemic toxicity compared to celastrol or TET used alone. TET-CTM/L showed the excellent potential for tumor-targeting ability in a biodistribution study.

Conclusion

Our study provides a new strategy for combining anti-cancer therapy that has good potential not only in the treatment of liver cancer but also can be applied to the treatment of other solid tumors.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

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.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Plant Alkaloid Tetrandrine Is a Nuclear Receptor 4A1 Antagonist and Inhibits Panc-1 Cell Growth In Vitro and In Vivo

The orphan nuclear receptor 4A1 (NR4A1) is highly expressed in human pancreatic cancer cells and exerts pro-oncogenic activity. In a previous study, we demonstrated that fangchinoline (FCN), a natural inhibitor of nuclear NR4A1, induces NR4A1-dependent apoptosis in human pancreatic cancer cells. In this study, we evaluated FCN and its structural analogs (berbamine, isotetrandrine, tetrandrine, and tubocurarine) for their inhibitory effects on NR4A1 transactivity, and confirmed that tetrandrine (TTD) showed the highest inhibitory effect in pancreatic cancer cells. Moreover, in a tryptophan fluorescence quenching assay, TTD directly bound to the ligand binding domain (LBD) of NR4A1 with a KD value of 10.60 μM. Treatment with TTD decreased proliferation and induced apoptosis in Panc-1 human pancreatic cancer cells in part through the reduced expression of the Sp1-dependent anti-apoptotic gene survivin and induction of ROS-mediated endoplasmic reticulum stress, which are the well-known NR4A1-dependent proapoptotic pathways. Furthermore, at a dose of 25 mg/kg/day, TTD reduced tumor growth in an athymic nude mouse xenograft model bearing Panc-1 cells. These data show that TTD is an NR4A1 antagonist and that modulation of the NR4A1-mediated pro-survival pathways is involved in the antitumor effects of TTD.

Natural Compound ZINC12899676 Reduces Porcine Epidemic Diarrhea Virus Replication by Inhibiting the Viral NTPase Activity

Porcine epidemic diarrhea virus (PEDV) is an alphacoronavirus (α-CoV) that causes high mortality in suckling piglets, leading to severe economic losses worldwide. No effective vaccine or commercial antiviral drug is readily available. Several replicative enzymes are responsible for coronavirus replication. In this study, the potential candidates targeting replicative enzymes (PLP2, 3CLpro, RdRp, NTPase, and NendoU) were screened from 187,119 compounds in ZINC natural products library, and seven compounds had high binding potential to NTPase and showed drug-like property. Among them, ZINC12899676 was identified to significantly inhibit the NTPase activity of PEDV by targeting its active pocket and causing its conformational change, and ZINC12899676 significantly inhibited PEDV replication in IPEC-J2 cells. It first demonstrated that ZINC12899676 inhibits PEDV replication by targeting NTPase, and then, NTPase may serve as a novel target for anti-PEDV.

Knowledge-based approaches to drug discovery for rare diseases

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.

Identification of Compounds Targeting HuD. Another Brick in the Wall of Neurodegenerative Disease Treatment

ELAV-like (ELAVL) RNA-binding proteins play a pivotal role in post-transcriptional processes, and their dysregulation is involved in several pathologies. This work was focused on HuD (ELAVL4), which is specifically expressed in nervous tissues, and involved in differentiation and synaptic plasticity mechanisms. HuD represents a new, albeit unexplored, candidate target for the treatment of several relevant neurodegenerative diseases. The aim of this pioneering work was the identification of new molecules able to recognize and bind HuD, thus interfering with its activity. We combined virtual screening, molecular dynamics (MD), and STD-NMR techniques. Starting from around 51 000 compounds, four promising hits eventually provided experimental evidence of their ability to bind HuD. Among the selected best hits, folic acid was found to be the most interesting one, being able to well recognize the HuD binding site. Our results provide a basis for the identification of new HuD interfering compounds which may be useful against neurodegenerative syndromes.

Simplex representation of molecular structure as universal QSAR/QSPR tool

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections

BACKGROUND

Viral diseases are responsible for several deaths around the world. Over the past few years, the world has seen several outbreaks caused by viral diseases that, for a long time, seemed to possess no risk. These are diseases that have been forgotten for a long time and, until nowadays, there are no approved drugs or vaccines, leading the pharmaceutical industry and several research groups to run out of time in the search for new pharmacological treatments or prevention methods. In this context, drug repurposing proves to be a fast and economically viable technique, considering the fact that it uses drugs that have a well-established safety profile. Thus, in this review, we present the main advances in drug repurposing and their benefit for searching new treatments against emerging viral diseases.

METHODS

We conducted a search in the bibliographic databases (Science Direct, Bentham Science, PubMed, Springer, ACS Publisher, Wiley, and NIH's COVID-19 Portfolio) using the keywords "drug repurposing", "emerging viral infections" and each of the diseases reported here (CoV; ZIKV; DENV; CHIKV; EBOV and MARV) as an inclusion/exclusion criterion. A subjective analysis was performed regarding the quality of the works for inclusion in this manuscript. Thus, the selected works were those that presented drugs repositioned against the emerging viral diseases presented here by means of computational, high-throughput screening or phenotype-based strategies, with no time limit and of relevant scientific value.

RESULTS

291 papers were selected, 24 of which were CHIKV; 52 for ZIKV; 43 for DENV; 35 for EBOV; 10 for MARV; and 56 for CoV and the rest (72 papers) related to the drugs repurposing and emerging viral diseases. Among CoV-related articles, most were published in 2020 (31 papers), updating the current topic. Besides, between the years 2003 - 2005, 10 articles were created, and from 2011 - 2015, there were 7 articles, portraying the outbreaks that occurred at that time. For ZIKV, similar to CoV, most publications were during the period of outbreaks between the years 2016 - 2017 (23 articles). Similarly, most CHIKV (13 papers) and DENV (14 papers) publications occur at the same time interval. For EBOV (13 papers) and MARV (4 papers), they were between the years 2015 - 2016. Through this review, several drugs were highlighted that can be evolved in vivo and clinical trials as possible used against these pathogens showed that remdesivir represent potential treatments against CoV. Furthermore, ribavirin may also be a potential treatment against CHIKV; sofosbuvir against ZIKV; celgosivir against DENV, and favipiravir against EBOV and MARV, representing new hopes against these pathogens.

CONCLUSION

The conclusions of this review manuscript show the potential of the drug repurposing strategy in the discovery of new pharmaceutical products, as from this approach, drugs could be used against emerging viral diseases. Thus, this strategy deserves more attention among research groups and is a promising approach to the discovery of new drugs against emerging viral diseases and also other diseases.

The Dimroth Rearrangement in the Synthesis of Condensed Pyrimidines - Structural Analogs of Antiviral Compounds

The review discusses the use of the Dimroth rearrangement in the synthesis of condensed pyrimidines which are key structural fragments of antiviral agents. The main attention is given to publications over the past 10 years. The bibliography includes 107 references.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Targeting ebola virus VP40 protein through novel inhibitors: exploring the structural and dynamic perspectives on molecular landscapes

Ebola filovirus (EBOV) is one of the deadliest known infectious agents, and a cause of Western African epidemics from 2013 to 2016. The virus has infected nearly 3000 humans and almost 1900 have died. In the past few years, various small molecules have been discovered to display efficiency against EBOV and some of them have progressed towards clinical trials. Even though continuous attempts have been made to find antiEBOV therapeutics, no potential drugs are yet approved against this viral infection. The development of small antiviral inhibitors has gained tremendous attention in the attempt to overcome EVD. With this background, we seek to offer molecular insights into EBOV VP40 protein inhibition, using all atom molecular mechanics methodology and binding free energy calculations. We have selected five novel reported inhibitors against VP40 protein, namely Comp1, Comp2, Comp3, Comp4, and Comp5, and explored their binding against the same target. It was evident from the analysis that all the inhibitors displayed stability in complex with VP40 protein; however, Comp1 exhibited enhanced stability and compactness. Comp1 unveiled favorable binding, which accounted for positive correlation motions in the active site residues. Likewise, Comp1 revealed the most promising binding (ΔG_bind - 40.3504 kcal/mol) as compared to the other four inhibitors, which disclosed relatively less favorable ΔG_bind. The highest binding energy of Comp1 to VP40 protein can be primarily endorsed to the upsurge in van der Waals energy by ΔE_vdW - 37.1609 kcal/mol and Coulomb energy by ΔE_ele - 52.7332 kcal/mol. Also, the hydrogen bond network is robust in Comp1-VP40 complex, with four hydrogen bonds, whilst it is less in other inhibitors. The outcomes from this report may assist in the advancement of novel VP40 inhibitors with high selectivity and potency for EVD therapeutics.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.

Evaluation of QSAR Equations for Virtual Screening

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

HER2-mediated enhancement of Ebola virus entry

Multiple cell surface molecules including TAM receptors (TYRO3, AXL, and MERTK), a family of tyrosine kinase receptors, can serve as attachment receptors for Ebola virus (EBOV) entry into cells. The interaction of these receptors with EBOV particles is believed to trigger the initial internalization events that lead to macropinocytosis. However, the details of how these interactions lead to EBOV internalization have yet to be elucidated. Here, we screened receptor tyrosine kinase (RTK) inhibitors for anti-EBOV activity by using our previously established biologically contained Ebola virus that lacks the VP30 gene (EBOVΔVP30) and identified several RTKs, including human epidermal growth factor receptor 2 (HER2), as potential targets of anti-EBOV inhibitors and as novel host factors that have a role in EBOV infection. Of these identified RTKs, it was only HER2 whose knockdown by siRNAs impaired EBOVΔVP30-induced AKT1 phosphorylation, an event that is required for AKT1 activation and subsequent macropinocytosis. Stable expression of HER2 resulted in constitutive activation of AKT1, resulting in the enhancement of EBOVΔVP30 growth, EBOV GP-mediated entry, and macropinocytosis. Moreover, we found that HER2 interacts with the TAM receptors, and in particular forms a complex with TYRO3 and EBOVΔVP30 particles on the cell surface. Interestingly, HER2 was required for EBOVΔVP30-induced TYRO3 and AKT1 activation, but the other TAM receptors (TYRO3 and MERTK) were not essential for EBOVΔVP30-induced HER2 and AKT1 activation. Our findings demonstrate that HER2 plays an important role in EBOV entry and provide novel insights for the development of therapeutics against the virus.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.

Tetrandrine: a review of its anticancer potentials, clinical settings, pharmacokinetics and drug delivery systems

OBJECTIVES

Tetrandrine, a natural bisbenzylisoquinoline alkaloid, possesses promising anticancer activities on diverse tumours. This review provides systematically organized information on cancers of tetrandrine in vivo and in vitro, discuss the related molecular mechanisms and put forward some new insights for the future investigations.

KEY FINDINGS

Anticancer activities of tetrandrine have been reported comprehensively, including lung cancer, colon cancer, bladder cancer, prostate cancer, ovarian cancer, gastric cancer, breast cancer, pancreatic cancer, cervical cancer and liver cancer. The potential molecular mechanisms corresponding to the anticancer activities of tetrandrine might be related to induce cancer cell apoptosis, autophagy and cell cycle arrest, inhibit cell proliferation, migration and invasion, ameliorate metastasis and suppress tumour cell growth. Pharmaceutical applications of tetrandrine combined with nanoparticle delivery system including liposomes, microspheres and nanoparticles with better therapeutic efficiency have been designed and applied encapsulate tetrandrine to enhance its stability and efficacy in cancer treatment.

SUMMARY

Tetrandrine was proven to have definite antitumour activities. However, the safety, bioavailability and pharmacokinetic parameter studies on tetrandrine are very limited in animal models, especially in clinical settings. Our present review on anticancer potentials of tetrandrine would be necessary and highly beneficial for providing guidelines and directions for further research of tetrandrine.

An overview of functional nanoparticles as novel emerging antiviral therapeutic agents

Research on highly effective antiviral drugs is essential for preventing the spread of infections and reducing losses. Recently, many functional nanoparticles have been shown to possess remarkable antiviral ability, such as quantum dots, gold and silver nanoparticles, nanoclusters, carbon dots, graphene oxide, silicon materials, polymers and dendrimers. Despite their difference in antiviral mechanism and inhibition efficacy, these functional nanoparticles-based structures have unique features as potential antiviral candidates. In this topical review, we highlight the antiviral efficacy and mechanism of these nanoparticles. Specifically, we introduce various methods for analyzing the viricidal activity of functional nanoparticles and the latest advances in antiviral functional nanoparticles. Furthermore, we systematically describe the advantages and disadvantages of these functional nanoparticles in viricidal applications. Finally, we discuss the challenges and prospects of antiviral nanostructures. This topic review covers 132 papers and will enrich our knowledge about the antiviral efficacy and mechanism of various functional nanoparticles.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

Perspectives towards antiviral drug discovery against Ebola virus

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC50 < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC50 = 4.53 μM) and the anticancer clinical candidate lucanthone (IC50 = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future.