Data Sets Representative of the Structures and Experimental Properties of FDA-Approved Drugs

Has pharmaceutical innovation reduced the average cost of U.S. health care episodes?

A number of authors have argued that technological innovation has increased U.S. health care spending. We investigate the impact that pharmaceutical innovation had on the average cost of U.S. health care episodes during the period 2000-2014, using data from the Bureau of Economic Analysis' Health Care Satellite Account and other sources. We analyze the relationship across approximately 200 diseases between the growth in the number of drugs that have been approved to treat the disease and the subsequent growth in the mean amount spent per episode of care, controlling for the growth in the number of episodes and other factors. Our estimates indicate that mean episode cost is not significantly related to the number of drugs ever approved 0-4 years before, but it is significantly inversely related to the number of drugs ever approved 5-20 years before. This delay is consistent with the fact (which we document) that utilization of a drug is relatively low during the first few years after it was approved, and that some drugs may have to be consumed for several years to have their maximum impact on treatment cost. Our estimates of the effect of pharmaceutical innovation on the average cost of health care episodes are quite insensitive to the weights used and to whether we control for 3 covariates. Our most conservative estimates imply that the drugs approved during 1986-1999 reduced mean episode cost by 4.7%, and that the drugs approved during 1996-2009 reduced mean episode cost by 2.1%. If drug approvals did not affect the number of episodes, the drugs approved during 1986-1999 would have reduced 2014 medical expenditure by about $93 billion. However, drug approvals may have affected the number, as well as the average cost, of episodes. We also estimate models of hospital utilization. The number of hospital days is significantly inversely related to the number of drugs ever approved 10-19 years before, controlling for the number of disease episodes. Our estimates imply that the drugs approved during 1984-1997 reduced the number of hospital days by 10.5%. The hospital cost reduction was larger than expenditure on the drugs.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

Currently Used Methods to Evaluate the Efficacy of Therapeutic Drugs and Kidney Safety

Kidney diseases with kidney failure or damage, such as chronic kidney disease (CKD) and acute kidney injury (AKI), are common clinical problems worldwide and have rapidly increased in prevalence, affecting millions of people in recent decades. A series of novel diagnostic or predictive biomarkers have been discovered over the past decade, enhancing the investigation of renal dysfunction in preclinical studies and clinical risk assessment for humans. Since multiple causes lead to renal failure, animal studies have been extensively used to identify specific disease biomarkers for understanding the potential targets and nephropathy events in therapeutic insights into disease progression. Mice are the most commonly used model to investigate the mechanism of human nephropathy, and the current alternative methods, including in vitro and in silico models, can offer quicker, cheaper, and more effective methods to avoid or reduce the unethical procedures of animal usage. This review provides modern approaches, including animal and nonanimal assays, that can be applied to study chronic nonclinical safety. These specific situations could be utilized in nonclinical or clinical drug development to provide information on kidney disease.

A perspective on the sharing of docking data

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Drug repurposing screens to identify potential drugs for chronic kidney disease by targeting prostaglandin E2 receptor

Renal inflammation and fibrosis are significantly correlated with the deterioration of kidney function and result in chronic kidney disease (CKD). However, current therapies only delay disease progression and have limited treatment effects. Hence, the development of innovative therapeutic approaches to mitigate the progression of CKD has become an attractive issue. To date, the incidence of CKD is still increasing, and the biomarkers of the pathophysiologic processes of CKD are not clear. Therefore, the identification of novel therapeutic targets associated with the progression of CKD is an attractive issue. It is a critical necessity to discover new therapeutics as nephroprotective strategies to stop CKD progression. In this research, we focus on targeting a prostaglandin E₂ receptor (EP2) as a nephroprotective strategy for the development of additional anti-inflammatory or antifibrotic strategies for CKD. The in silico study identified that ritodrine, dofetilide, dobutamine, and citalopram are highly related to EP2 from the results of chemical database virtual screening. Furthermore, we found that the above four candidate drugs increased the activation of autophagy in human kidney cells, which also reduced the expression level of fibrosis and NLRP3 inflammasome activation. It is hoped that these findings of the four candidates with anti-NLRP3 inflammasome activation and antifibrotic effects will lead to the development of novel therapies for patients with CKD in the future.

Computational model for lipid binding regions in phospholipase (Ves a 1) from Vespa venom

The Thai banded tiger wasp (Vespa affinis) is a dangerous vespid species found in Southeast Asia, and its stings often result in fatalities due to the presence of lethal phospholipase A[Formula: see text], known as Vespapase or Ves a 1. Developing anti-venoms for Ves a 1 using chemical drugs, such as chemical drug guide, remains a challenging task. In this study, we screened 2056 drugs against the opening conformation of the venom using the ZINC 15 and e-Drug 3D databases. The binding free energy of the top five drug candidates complexed with Ves a 1 was calculated using 300-ns-MD trajectories. Our results revealed that voxilaprevir had a higher binding free energy at the catalytic sites than other drug candidates. Furthermore, the MD simulation results indicated that voxilaprevir formed stable conformations within the catalytic pocket. Consequently, voxilaprevir could act as a potent inhibitor, opening up avenues for the development of more effective anti-venom therapeutics for Ves a 1.

Combined and independent effects of OCT1 and CYP2D6 on the cellular disposition of drugs

The organic cation transporter 1 (OCT1) mediates the cell uptake and cytochrome P450 2D6 (CYP2D6) the metabolism of many cationic substrates. Activities of OCT1 and CYP2D6 are affected by enormous genetic variation and frequent drug-drug interactions. Single or combined deficiency of OCT1 and CYP2D6 might result in dramatic differences in systemic exposure, adverse drug reactions, and efficacy. Thus, one should know what drugs are affected to what extent by OCT1, CYP2D6 or both. Here, we compiled all data on CYP2D6 and OCT1 drug substrates. Among 246 CYP2D6 substrates and 132 OCT1 substrates, we identified 31 shared substrates. In OCT1 and CYP2D6 single and double-transfected cells, we studied which, OCT1 or CYP2D6, is more critical for a given drug and whether there are additive, antagonistic or synergistic effects. In general, OCT1 substrates were more hydrophilic than CYP2D6 substrates and smaller in size. Inhibition studies showed unexpectedly pronounced inhibition of substrate depletion by shared OCT1/CYP2D6 inhibitors. In conclusion, there is a distinct overlap in the OCT1/CYP2D6 substrate and inhibitor spectra, so in vivo pharmacokinetics and -dynamics of shared substrates may be significantly affected by frequent OCT1- and CYP2D6-polymorphisms and by comedication with shared inhibitors.

Ligand-based virtual screening, molecular dynamics, and biological evaluation of repurposed drugs as inhibitors of Trypanosoma cruzi proteasome

Chagas disease is a well-known Neglected Tropical Disease, mostly endemic in continental Latin America, but that has spread to North America and Europe. Unfortunately, current treatments against such disease are ineffective and produce known and undesirable side effects. To find novel effective drug candidates to treat Chagas disease, we uniquely explore the Trypanosoma cruzi proteasome as a recent biological target and, also, apply drug repurposing through different computational methodologies. For this, we initially applied protein homology modeling to build a robust model of proteasome β4/β5 subunits, since there is no crystallographic structure of this target. Then, we used it on a drug repurposing via a virtual screening campaign starting with more than 8,000 drugs and including the methodologies: ligand-based similarity, toxicity predictions, and molecular docking. Three drugs were selected concerning their favorable interactions at the protein binding site and subsequently submitted to molecular dynamics simulations, which allowed us to elucidate their behavior and compare such theoretical results with experimental ones, obtained in biological assays also described in this paper.Communicated by Ramaswamy H. Sarma.

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

Substrates and Inhibitors of the Organic Cation Transporter 3 and Comparison with OCT1 and OCT2

Organic cation transporters (OCTs) 1, 2, and 3 facilitate cellular uptake of structurally diverse endogenous and exogenous substances. However, their substrate and inhibitor specificity are not fully understood. We performed a broad in vitro screening for OCT3 substrates and inhibitors, allowing us to compare the substrate spectra and to study the relationship between transport and inhibition of transport. Generally, substrates were smaller and more hydrophilic than OCT3 inhibitors. The best model-based predictor of transport was the positive charge, while the best predictor of inhibition was the aromatic ring count. OCT3 inhibition was well correlated between different model substrates. Substrates of OCT3 were mainly weak inhibitors, and the best inhibitors were not substrates. As tested with 264 substances, OCT3 transport had significantly more overlap with OCT2 than OCT1. Our data further substantiate that specificity of OCT transport varies with minor substitutions rather than with the general scaffolds of substrates.

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases

Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.

Targeting the BspC-vimentin interaction to develop anti-virulence therapies during Group B streptococcal meningitis

Bacterial infections are a major cause of morbidity and mortality worldwide and the rise of antibiotic resistance necessitates development of alternative treatments. Pathogen adhesins that bind to host cells initiate disease pathogenesis and represent potential therapeutic targets. We have shown previously that the BspC adhesin in Group B Streptococcus (GBS), the leading cause of bacterial neonatal meningitis, interacts with host vimentin to promote attachment to brain endothelium and disease development. Here we determined that the BspC variable (V-) domain contains the vimentin binding site and promotes GBS adherence to brain endothelium. Site directed mutagenesis identified a binding pocket necessary for GBS host cell interaction and development of meningitis. Using a virtual structure-based drug screen we identified compounds that targeted the V-domain binding pocket, which blocked GBS adherence and entry into the brain in vivo. These data indicate the utility of targeting the pathogen-host interface to develop anti-virulence therapeutics.

Understanding COVID-19 Pathogenesis: A Drug-Repurposing Effort to Disrupt Nsp-1 Binding to Export Machinery Receptor Complex

Non-structural protein 1 (Nsp1) is a virulence factor found in all beta coronaviruses (b-CoVs). Recent studies have shown that Nsp1 of SARS-CoV-2 virus interacts with the nuclear export receptor complex, which includes nuclear RNA export factor 1 (NXF1) and nuclear transport factor 2-like export factor 1 (NXT1). The NXF1-NXT1 complex plays a crucial role in the transport of host messenger RNA (mRNA). Nsp1 interferes with the proper binding of NXF1 to mRNA export adaptors and its docking to the nuclear pore complex. We propose that drugs targeting the binding surface between Nsp1 and NXF1-NXT1 may be a useful strategy to restore host antiviral gene expression. Exploring this strategy forms the main goals of this paper. Crystal structures of Nsp1 and the heterodimer of NXF1-NXT1 have been determined. We modeled the docking of Nsp1 to the NXF1-NXT1 complex, and discovered repurposed drugs that may interfere with this binding. To our knowledge, this is the first attempt at drug-repurposing of this complex. We used structural analysis to screen 1993 FDA-approved drugs for docking to the NXF1-NXT1 complex. The top hit was ganirelix, with a docking score of -14.49. Ganirelix competitively antagonizes the gonadotropin releasing hormone receptor (GNRHR) on pituitary gonadotrophs, and induces rapid, reversible suppression of gonadotropin secretion. The conformations of Nsp1 and GNRHR make it unlikely that they interact with each other. Additional drug leads were inferred from the structural analysis of this complex, which are discussed in the paper. These drugs offer several options for therapeutically blocking Nsp1 binding to NFX1-NXT1, which may normalize nuclear export in COVID-19 infection.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

Identification of known drugs as potential SARS-CoV-2 Mpro inhibitors using ligand- and structure-based virtual screening

Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment – in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy – application of ligand- and structure-based virtual screening – using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

A review on drug repurposing applicable to COVID-19

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

Chemoinformatic Analysis of Psychotropic and Antihistaminic Drugs in the Light of Experimental Anti-SARS-CoV-2 Activities

Introduction

There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients.

Objective

Based upon clinical observations, we proposed that some psychotropic and antihistaminic drugs could protect psychiatric patients from SARS-CoV-2 infection. This observation is investigated in the light of experimental in vitro data on SARS-CoV-2.

Methods

SARS-CoV-2 high-throughput screening results are available at the NCATS COVID-19 portal. We investigated the in vitro anti-viral activity of many psychotropic and antihistaminic drugs using chemoinformatics approaches.

Results and Discussion

We analyze our clinical observations in the light of SARS-CoV-2 experimental screening results and propose that several cationic amphiphilic psychotropic and antihistaminic drugs could protect people from SARS-CoV-2 infection; some of these molecules have very limited adverse effects and could be used as prophylactic drugs. Other cationic amphiphilic drugs used in other disease areas are also highlighted. Recent analyses of patient electronic health records reported by several research groups indicate that some of these molecules could be of interest at different stages of the disease progression. In addition, recently reported drug combination studies further suggest that it might be valuable to associate several cationic amphiphilic drugs. Taken together, these observations underline the need for clinical trials to fully evaluate the potentials of these molecules, some fitting in the so-called category of broad-spectrum antiviral agents. Repositioning orally available drugs that have moderate side effects and should act on molecular mechanisms less prone to drug resistance would indeed be of utmost importance to deal with COVID-19.

Repurposing of approved drugs with potential to interact with SARS-CoV-2 receptor

Respiratory transmission is the primary route of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Angiotensin I converting enzyme 2 (ACE2) is the known receptor of SARS-CoV-2 surface spike glycoprotein for entry into human cells. A recent study reported absent to low expression of ACE2 in a variety of human lung epithelial cell samples. Three bioprojects (PRJEB4337, PRJNA270632 and PRJNA280600) invariably found abundant expression of ACE1 (a homolog of ACE2 and also known as ACE) in human lungs compared to very low expression of ACE2. In fact, ACE1 has a wider and more abundant tissue distribution compared to ACE2. Although it is not obvious from the primary sequence alignment of ACE1 and ACE2, comparison of X-ray crystallographic structures show striking similarities in the regions of the peptidase domains (PD) of these proteins, which is known (for ACE2) to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Critical amino acids in ACE2 that mediate interaction with the viral spike protein are present and organized in the same order in the PD of ACE1. In silico analysis predicts comparable interaction of SARS-CoV-2 spike protein with ACE1 and ACE2. In addition, this study predicts from a list of 1263 already approved drugs that may interact with ACE2 and/or ACE1 and potentially interfere with the entry of SARS-CoV-2 inside the host cells.

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Peptidase domains (PD) of ACE1 and ACE2 have striking similarities.

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In silico analysis predicts comparable interactions of S protein with ACE1 and ACE2.

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Several approved drugs may be repurposed to interfere with SARS-CoV-2 binding.

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterized them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein-ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are implemented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at https://sars3d.com/. This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.

Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants

The COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br .

Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets

The acute respiratory syndrome caused by the SARS-CoV-2, known as COVID-19, has been ruthlessly tormenting the world population for more than six months. However, so far no effective drug or vaccine against this plague have emerged yet, despite the huge effort in course by researchers and pharmaceutical companies worldwide. Willing to contribute with this fight to defeat COVID-19, we performed a virtual screening study on a library containing Food and Drug Administration (FDA) approved drugs, in a search for molecules capable of hitting three main molecular targets of SARS-CoV-2 currently available in the Protein Data Bank (PDB). Our results were refined with further molecular dynamics (MD) simulations and MM-PBSA calculations and pointed to 7 multi-target hits which we propose here for experimental evaluation and repurposing as potential drugs against COVID-19. Additional rounds of docking, MD simulations and MM-PBSA calculations with remdesivir suggested that this compound can also work as a multi-target drug against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Identification of novel potential ricin inhibitors by virtual screening, molecular docking, molecular dynamics and MM-PBSA calculations: a drug repurposing approach

Ricin is a potent cytotoxin with no available antidote. Its catalytic subunit, RTA, damages the ribosomal RNA (rRNA) of eukaryotic cells, preventing protein synthesis and eventually leading to cell death. The combination between easiness of obtention and high toxicity turns ricin into a potential weapon for terrorist attacks, urging the need of discovering effective antidotes. On this context, we used computational techniques, in order to identify potential ricin inhibitors among approved drugs. Two libraries were screened by two different docking algorithms, followed by molecular dynamics simulations and MM-PBSA calculations in order to corroborate the docking results. Three drugs were identified as potential ricin inhibitors: deferoxamine, leucovorin and plazomicin. Our calculations showed that these compounds were able to, simultaneously, form hydrogen bonds with residues of the catalytic site and the secondary binding site of RTA, qualifying as potential antidotes against intoxication by ricin.Communicated by Ramaswamy H. Sarma.

Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

Molecular basis of COVID-19 pathogenesis

The review summarizes the publications, available at the time it was written, addressing the chemical and biological processes that occur in the human body upon exposure to coronaviruses, in particular SARS-CoV-2. The mechanisms of viral particle entry into the cell, viral replication and impact on the immune system and on oxygen transport system are considered. The causes behind complications of the viral infection, such as vasculitis, thrombosis, cytokine storm and lung fibrosis, are discussed. The latest research in the field of small molecule medications to counteract the virus is surveyed. Molecular targets and possible vectors to exploit them are considered. The review is primarily written for specialists who want to understand the chains of activation, replication, action and inhibition of SARS-CoV-2. Due to the short period of such studies, the data on complexes of small molecule compounds with possible protein targets are not numerous, but they will be useful in the search and synthesis of new potentially effective drugs.

The bibliography includes 144 references.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

Computational identification of disulfiram and neratinib as putative SARS-CoV-2 main protease inhibitors

Identification of disulfiram and neratinib as putative covalent inhibitors of SARS-CoV-2 virus main protease M^pro by a combination of ‘on-top docking’ procedure, expert evaluation of potential hits and molecular dynamics is reported herein. This finding shows the importance of further development of virtual screening add-ons.

In silico analysis and identification of promising hits against 2019 novel coronavirus 3C-like main protease enzyme

The recent outbreak of the 2019 novel coronavirus disease (COVID-19) has been proved as a global threat. No particular drug or vaccine has not yet been discovered which may act specifically against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and causes COVID-19. For this highly infectious virus, 3CL-like main protease (3CL^pro) plays a key role in the virus life cycle and can be considered as a pivotal drug target. Structure-based virtual screening of DrugBank database resulted in 20 hits against 3CL^pro. Atomistic 100 ns molecular dynamics of five top hits and binding energy calculation analyses were performed for main protease-hit complexes. Among the top five hits, Nafarelin and Icatibant affirmed the binding energy (g_MMPBSA) of -712.94 kJ/mol and -851.74 kJ/mol, respectively. Based on binding energy and stability of protein-ligand complex; the present work reports these two drug-like hits against SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus' replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

Epigenetic drug discovery: systematic assessment of chemical space

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 10⁶ ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 10⁶ compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.

To Pass or Not To Pass: Predicting the Blood-Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory

Predicting the ability of chemical species to cross the blood-brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory. We have been able to show, for the first time, that the computed excess chemical potential in different solvents can be successfully used to predict permeability of compounds in a binary manner (yes/no) via a minimum-descriptor-based model. Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure-activity relationship modeling. The workflow presented in this work is simple enough to be used by nonexperts with ease.