high-throughput-antiviral-assays-to-screen-for-inhibitors-zika-virus

High-throughput Antiviral Assays to Screen for Inhibitors of Zika Virus Replication

Antiviral drug discovery requires the development of reliable biochemical and cellular assays that can be performed in high-throughput screening (HTS) ...

University of Sao Paulo

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and ) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.

Quantum Machine Learning Algorithms for Drug Discovery Applications.

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC 3.2 μM and CC 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for  testing by several groups. These have led to a growing database of molecules with  activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2  inhibition data and used them to prioritize additional FDA-approved compounds for  testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding ( 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active . This combined machine learning and  testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

The COVID-19 outbreak has rapidly spread on a global scale, affecting the economy and public health systems throughout the world. In recent years, peptide-based therapeutics have been widely studied and developed to treat infectious diseases, including viral infections. Herein, the antiviral effects of the lysine linked dimer des-Cys, Lys,Lys-(pBthTX-I)K () and derivatives against SARS-CoV-2 are reported. The lead peptide  and derivatives showed attractive inhibitory activities against SARS-CoV-2 (EC = 28-65 µM) and mostly low cytotoxic effect (CC > 100 µM). To shed light on the mechanism of action underlying the peptides' antiviral activity, the Main Protease (M) and Papain-Like protease (PL) inhibitory activities of the peptides were assessed. The synthetic peptides showed PL inhibition potencies (ICs = 1.0-3.5 µM) and binding affinities ( = 0.9-7 µM) at the low micromolar range but poor inhibitory activity against M (IC > 10 µM). The modeled binding mode of a representative peptide of the series indicated that the compound blocked the entry of the PL substrate toward the protease catalytic cleft. Our findings indicated that non-toxic dimeric peptides derived from the Bothropstoxin-I have attractive cellular and enzymatic inhibitory activities, thereby suggesting that they are promising prototypes for the discovery and development of new drugs against SARS-CoV-2 infection.

Article	Year
Quantum Machine Learning Algorithms for Drug Discovery Applications. Journal of chemical information and modeling\| PubMed ID: 34032436	2021
Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus. Journal of chemical information and modeling\| PubMed ID: 34286575	2021
Machine Learning Models Identify Inhibitors of SARS-CoV-2. Journal of chemical information and modeling\| PubMed ID: 34387990	2021
Non-Toxic Dimeric Peptides Derived from the Bothropstoxin-I Are Potent SARS-CoV-2 and Papain-like Protease Inhibitors. Molecules (Basel, Switzerland)\| PubMed ID: 34443484	2021

Victor O. Gawriljuk

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