D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19
Yanqing YangDeshan ZhouXinben ZhangYulong ShiJiaxin HanLiping ZhouLeyun WuMinfei MaJintian LiShaoliang PengZhijian XuWeiliang Zhu
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Abstract:
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.Keywords:
DrugBank
Repurposing
Drug repositioning
False Discovery Rate
Identification
The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results. PMID: 34313439
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Objective: SARS-CoV-2 is a pandemic virus characterized by upper respiratory tract infection and can range from mild symptoms to severe complications. In this case, drug repurposing and computer-aided studies have become very important to find emergency solutions. In this study, drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Material and Method: Comprehensive virtual screening and molecular docking studies from 8820 drug molecules or candidates obtained from the DrugBank database were performed on the RNA binding protein, 2'-O-methyltransferase, and endoribonuclease of SARS-CoV-2; and potential drug candidates were determined for each target. Virtual screening studies have been done with High-Throughput Virtual Screening (HTVS), Standard Precision (SP), Extra Precision (XP), and Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Also, information about the clinical findings, transmission, pathogenesis, and treatment of SARS-CoV-2 has been given. Result and Discussion: Drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Potential compound recommendations for each drug target were presented. Information was given about key amino acids where active sites of drug target proteins interact with ligands. This study is expected to be useful in target-based drug development studies on the proteins of SARS-CoV-2.
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To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.
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The discovery of drug compounds has a long history in drug repurposing, notably by fortuitous findings. It has taken a new path in the creation of novel therapeutics based on existent or authorized drugs in recent years. Importantly, our knowledge of cancer biology and the related cancer hallmarks is growing. This, together with repurposing studies that use modern bioinformatics and comprehensive screening of the complete pharmacopeia, should lead to the discovery of novel medicines and targets. Furthermore, the usage of non-oncology pharmaceuticals, which make up most of our treatments, has the potential to speed up drug repurposing even further. We looked at both phenotypic-based and target-based methods of medication repurposing as well as described and assessed old non-oncology medications as prospective candidates for drug repurposing based on a broad knowledge of these principles and associated investigations of drug repurposing over the previous decade. Some of these medications successfully regulate at least one characteristic of cancer, whereas the others have a broad anticancer activity by regulating several targets through different signaling pathways, which is often brought on by various simultaneous signaling pathways. Furthermore, the emergence of computerized databases of disease gene targets, functional readouts, and clinical data encompassing inter-individual genetic variants and toxicities has allowed an alternative "big data" approach to grow at an unheard-of rate during the past decade. Here, we review the sources that are now on hand and speculate on significant upside possibilities.
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Alzheimer’s disease is one of the leading causes of death globally, significantly impacting countless families and communities. In parallel, recent advancements in molecular biology and network approaches, guided by the Network Medicine perspective, offer promising outcomes for Alzheimer’s disease research and treatment. In this study, we aim to discover candidate therapies for AD through drug repurposing. We combined a protein-protein interaction (PPI) network with drug-target interactions. Experimentally validated PPI data were collected from the PICKLE meta-database, while drugs and their protein targets were sourced from the DrugBank database. Then, based on RNA-Seq data, we first assigned weights to edges to indicate co-expression, and secondly, estimated differential gene expression to select a subset of genes potentially related to the disease. Finally, small subgraphs (modules) were extracted from the graph, centered on the genes of interest. The analysis revealed that even if there is no drug targeting several genes of interest directly, an existing drug might target a neighboring node, thus indirectly affecting the aforementioned genes. Our approach offers a promising method for treating various diseases by repurposing existing drugs, thereby reducing the cost and time of experimental procedures and paving the way for more precise Network Medicine strategies.
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This article describes the Literature-Related Discovery technique and its application to Treatment Repurposing (which includes, but goes well beyond, Drug Repurposing). Illustrative results of potential repurposed treatments were shown from a study on preventing and reversing Alzheimer’s disease. The detailed query used to generate these results is presented. The approach has the potential to identify voluminous amounts of candidate treatments for repurposing. Additionally, a broad review of the Drug Repurposing literature is provided. A Drug Repurposing database is retrieved and the structure and content are analyzed using Text Clustering and Factor Analysis. Two taxonomies of the Drug Repurposing literature are presented and specific major themes are shown.
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Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.
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Phenols are widely distributed in various plants and plant-derived foods. Currently, there is an increasing interest in their application as food supplements. In this study we performed a virtual screening to identify potential molecular targets of phenolic compounds derived from medicinal plants known for their antioxidant and anticancer effects. A dataset of 75 phenols, reported in the literature and a virtual library of 7770 unique drug compounds, extracted from the DrugBank database (https://www.drugbank.ca/) were used. Multi-conformer structure databases were created using OpenEye OMEGA, shape- and chemical-based overlays of the conformers were performed in OpenEye ROCS (https://www.eyesopen.com/). As a result of the virtual screening, followed by data filtration and analysis, two bacterial enzymes, responsible for DNA replication, were suggested as potential novel targets of a plant-derived hydroxyanthraquinone. This research allows outlining the potential receptor-mediated pharmacological mechanisms of phenolic compounds and aims to be a first step in the development of in silico protocol for their prioritisation as healthy dietary supplements.
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