Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking.

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

ChemFlow_py: a flexible toolkit for docking and rescoring.

The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA.

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

Probing CAS database as prospective antiviral agents against SARS-CoV-2 main protease.

The pandemic of COVID-19 has an unprecedented impact on global health and economy. The novel SARS-CoV-2 is recognized as the etiological agent of current outbreak. Because of its contagious human-to-human transmission, it is an utmost global health emergency at present. To mitigate this threat many scientists and researchers are racing to develop antiviral therapy against the virus. Unfortunately, to date no vaccine or antiviral therapeutic is approved thus there is an urgent need to discover antiviral agent to help the individual who are at high risk. Virus main protease or chymotrypsin-like protease plays a pivotal role in virus replication and transcription; thus, it is considered as an attractive drug target to combat the COVID-19. In this study, multistep structure based virtual screening of CAS antiviral database is performed for the identification of potent and effective small molecule inhibitors against chymotrypsin-like protease of SARS-CoV-2. Consensus scoring strategy combine with flexible docking is used to extract potential hits. As a result of extensive virtual screening, 4 hits were shortlisted for MD simulation to study their stability and dynamic behavior. Insight binding modes demonstrated that the selected hits stabilized inside the binding pocket of the target protein and exhibit complementarity with the active site residues. Our study provides compounds for further in vitro and in vivo studies against SARS-CoV-2.

Discovery of a potent and selective adenylyl cyclase type 8 agonist by docking-based virtual screening.

Adenylyl cyclases (ACs), which are responsible for catalyzing the conversion of adenosine triphosphate (ATP) into the second messenger cyclic adenosine monophosphate (cAMP), play a critical role in cell signal transduction. In this study, a combined approach involving docking-based virtual screening, with the combination of homology modeling followed by an in-vitro, and cell-based biological assay have been performed for discovering a class of novel potent and selective isoform adenylyl cyclase type 8 (AC8) agonist. The computer-aided virtual screening was used to identify fourteen virtual cluster compounds as potential hits which were further subjected to rigorous bioassays. A novel hit compound VHC-7 (ethyl 3-(2,4-dichlorobenzyl)-2-oxoindoline-3-carboxylate) was identified as a highly potent selective AC8 agonist with EC50 value of 0.1052 ± 0.038 µM. Remarkably, the molecule herein reported can be explored further to discover greater number of hit compounds with better pharmacokinetic properties as well as to serve as a promising novel hit agonist of AC8 for the treatment of various central nervous system disorders and its associated diseases.

Rational creation and systematic analysis of cervical cancer kinase-inhibitor binding profile.

The kinase-regulatory cell signaling networks play a central role in the pathogenesis of human cervical cancer (hCC). However, only few kinase inhibitors have been successfully developed for treatment of this cancer to date. Considering that the active sites of protein kinases are highly conserved and small-molecule inhibitors should generally exhibit high promiscuity and broad specificity across the hCC-related kinase array, it is supposed that the established kinase targets of hCC can be targeted unexpectedly by certain noncognate kinase inhibitors. This provides a novel idea to practice the new uses for old drugs in anti-cancer chemotherapy. Here, we create a systematic kinase-inhibitor binding profile in a high-throughput manner by molecular docking and consensus scoring, where the kinases have been collected as therapeutic targets of hCC and the inhibitors are reversible, ATP-competitive and readily available. The docking/scoring scheme is tested rigorously with structure-solved and affinity-known kinase-inhibitor complex samples, which is later demonstrated to be effective in inferring unexpected inhibitor response to hCC-related kinases. Few promising kinase-inhibitor pairs are identified from the profile and tested experimentally at cellular and molecular levels. It is found that the kinase-inhibitor promiscuity is a common phenomenon but only few can interaction effectively and inhibit potently. In addition, the high-scoring inhibitors generally exhibit good suppressing potency on hCC cell viability as compared to those low-scoring ones, imparting that the created profile can well reflect the tumor cytotoxicity of noncognate kinase inhibitors. A further kinase assay suggests that the ErbB family kinases are the potential targets of these high-scoring inhibitors, with noncognate inhibitory activity up to nanomolar level. Structure analysis reveals that the nonbonded interactions of potent noncogante kinase-inhibitor binding can divided into a polar tail and a nonpolar lobe, which confer specificity and stability to the binding, respectively.

Structure based inhibitor design targeting glycogen phosphorylase B. Virtual screening, synthesis, biochemical and biological assessment of novel N-acyl-ß-d-glucopyranosylamines.

Glycogen phosphorylase (GP) is a validated target for the development of new type 2 diabetes treatments. Exploiting the Zinc docking database, we report the in silico screening of 1888 N-acyl-ß-d-glucopyranosylamines putative GP inhibitors differing only in their R groups. CombiGlide and GOLD docking programs with different scoring functions were employed with the best performing methods combined in a 'consensus scoring' approach to ranking of ligand binding affinities for the active site. Six selected candidates from the screening were then synthesized and their inhibitory potency was assessed both in vitro and ex vivo. Their inhibition constants' values, in vitro, ranged from 5 to 377µM while two of them were effective at causing inactivation of GP in rat hepatocytes at low µM concentrations. The crystal structures of GP in complex with the inhibitors were defined and provided the structural basis for their inhibitory potency and data for further structure based design of more potent inhibitors.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach.

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced 'w_new', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data.

BACKGROUND: Gaucher disease (GD) is a rare autosomal recessive condition associated with clinical features such as splenomegaly, hepatomegaly, anemia, thrombocytopenia, and bone abnormalities. Three clinical forms of GD have been defined based on the absence (type 1, GD1) or presence (types 2 and 3) of neurological signs. Early diagnosis can reduce the likelihood of severe, often irreversible complications. The aim of this study was to validate the ability of factors from the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system to discriminate between patients with GD1 and controls using real-world data from electronic patient medical records from Maccabi Healthcare Services, Israel's second-largest state-mandated healthcare provider. METHODS: We applied the GED-C scoring system to 265 confirmed cases of GD and 3445 non-GD controls matched for year of birth, sex, and socioeconomic status identified from 1998 to 2022. The analyses were based on two databases: (1) all available data and (2) all data except free-text notes. Features from the GED-C scoring system applicable to GD1 were extracted for each individual. Patients and controls were compared for the proportion of the specific features and overall GED-C scores. Decision tree and random forest models were trained to identify the main features distinguishing GD from non-GD controls. RESULTS: The GED-C scoring distinguished individuals with GD from controls using both databases. Decision tree models for the databases showed good accuracy (0.96 [95% CI 0.95-0.97] for Database 1; 0.95 [95% CI 0.94-0.96] for Database 2), high specificity (0.99 [95% CI 0.99-1]) for Database 1; 1.0 [95% CI 0.99-1] for Database 2), but relatively low sensitivity (0.53 [95% CI 0.46-0.59] for Database 1; 0.32 [95% CI 0.25-0.38]) for Database 2). The clinical features of splenomegaly, thrombocytopenia (< 50 × 109/L), and hyperferritinemia (300-1000 ng/mL) were found to be the three most accurate classifiers of GD in both databases. CONCLUSION: In this analysis of real-world patient data, certain individual features of the GED-C score discriminate more successfully between patients with GD and controls than the overall score. An enhanced diagnostic model may lead to earlier, reliable diagnoses of Gaucher disease, aiming to minimize the severe complications associated with this disease.

The discrimination between autoimmune glial fibrillary acidic protein astrocytopathy and tuberculous meningitis.

OBJECTIVE: The differential diagnosis between autoimmune glial fibrillary acidic protein astrocytopathy (AGFAPA) mimicking tuberculous meningitis and tuberculous meningitis (TBM) remains challenging in clinical practice. This study aims to identify the clinical, laboratory parameters, and clinical score systems that may be helpful in differentiating AGFAPA from TBM. METHOD: Overall 22 AGFAPA patients who were initially misdiagnosed as TBM (AGFAPA-TBM) and 30 confirmed TBM patients were included. The clinical, laboratory, imaging parameters, Thwaites systems, and Lancet consensus scoring systems (LCSS) of all patients were reviewed. Logistic regression was employed to establish a diagnostic formula to differentiate AGFAPA-TBM from TBM. The receiver operating characteristic (ROC) curve was applied to determine the best diagnostic critical point of the formula. RESULTS: Urinary retention was more frequent in AGFAPA-TBM patients (72.7% vs 33.3%, p = 0.012). A significantly lower ratio of T-SPOT. TB was noted in AGFAPA-TBM patients (9.1% vs 82.1%, p < 0.001). We found the LCSS was able to differentiate AGFAPA-TBM from TBM (AUC value 0.918, 95% CI=0.897-0.924). Furthermore, we set up a new scoring system with three variables: urinary retention, T-SPOT. TB, and cerebral imaging criteria in LCSS. The proposed diagnostic score ranges from -8 to 2, and a score of ≥ 0 was suggestive of AGFAPA-TBM (AUC value 0.938, 95% CI=0.878-0.951). CONCLUSIONS: This study is the first to evaluate the Thwaites system and LCSS in AGFAPA-TBM and TBM. We provide an alternative diagnostic formula to differentiate AGFAPA-TBM from TBM and suggest testing for GFAP antibodies to avoid misdiagnosis when this scoring system meets AGFAPA-TBM.

Identification and optimization of TDP1 inhibitors from anthraquinone and chalcone derivatives: consensus scoring virtual screening and molecular simulations.

Virtual screening aims to identify and rank compounds with drug/lead-like properties based on their affinity for the protein target. We developed a methodology that integrates structure- and ligand-based screening approaches to enhance hit rates against the TDP1 protein within a database of anthraquinone and chalcone derivatives, followed by evaluation of prioritized compounds through molecular simulations. This technique is particularly useful for training set imbalances. Four screening methods were used: QSAR, pharmacophore, shape similarity, and docking. Each method was individually trained to score compounds, and the scores were fused to create parallel Z-score fusion. The QSAR models exhibited satisfactory R2 values (0.84 to 0.75), whereas the pharmacophoric and shape similarity models demonstrated excellent performance (ROC:0.82-0.88). Docking enrichment analysis identified 6N0D as the optimal TDP1 crystal structure (ROC = 0.73). Remarkably, the consensus scoring method surpassed other screening methods, achieving the highest ROC value of 0.98. Docking screening prioritized compounds with binding modes resembling the co-crystallized ligands, whereas MMGBSA, consensus, and docking produced dynamic simulations that were as stable as the co-crystallized ligands. Additionally, the QSAR-selected compounds exhibited binding modes similar to those of commercially available TDP1 inhibitors. In this study, a strong correlation was found between the inhibitory concentrations and binding energy values of commercialized TDP1 inhibitors, indicating that the top-ranked compounds are expected to have potent inhibitory effects in the nano-/micromolar range. The results of this study establish that consensus scoring can be used as an adaptable mainstay virtual screening methodology, pending subsequent experimental validation for affirmation.Communicated by Ramaswamy H. Sarma.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening.

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform.

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Computational Evolution Protocol for Peptide Design.

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors.

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

In silico Repurposing of Drugs for pan-HDAC and pan-SIRT Inhibitors: Consensus Structure-based Virtual Screening and Pharmacophore Modeling Investigations.

OBJECTIVES: Drug repurposing is a highly popular approach to find new indications for drugs, which greatly reduces time and costs for drug design and discovery. Non-selective inhibitors of histone deacetylase (HDAC) isoforms including sirtuins (SIRTs) are effective against conditions like cancer. In this study, we used molecular docking to screen Food and Drug Administration (FDA)-approved drugs to identify a number of drugs with a potential to be repurposed for pan-HDAC and pan-SIRT inhibitor activity. MATERIALS AND METHODS: The library of FDA-approved drugs was optimized using MacroModel. The crystal structures of HDAC1-4, 6-8, SIRT1-3, 5, 6 were prepared before the library was docked to each structure using Glide, FRED, and AutoDock Vina/PyRx. Consensus scores were derived from the docking scores obtained from each software. Pharmacophore modeling was performed using Phase. RESULTS: Based on the consensus scores, belinostat, bexarotene, and cianidanol emerged as top virtual pan-HDAC inhibitors whereas alosetron, cinacalcet, and indacaterol emerged as virtual pan-SIRT inhibitors. Pharmacophore hypotheses for these virtual inhibitors were also suggested through pharmacophore modeling in agreement with the molecular docking models. CONCLUSION: The consensus approach enabled selection of the best performing drug molecules according to different software, and good scores against isoforms (virtual pan-HDAC and pan-SIRT inhibitors). The study not only proposes potential drugs to be repurposed for HDAC and SIRT-related diseases but also provides insights for designing potent de novo derivatives.

Discovery of potent α-glucosidase inhibitors through structure-based virtual screening of an in-house azole collection.

Diabetes mellitus, a chronic disorder characterized by hyperglycemia, is considered a pandemic of modern times. α-Glucosidase inhibitors emerged as a promising class of antidiabetic drugs with better tolerability compared with its alternatives. Azoles, although widely preferred in drug design, have scarcely been investigated for their potential against α-glucosidase. In this study, we evaluated α-glucosidase inhibitory effects 20 azole derivatives selected out of an in-house collection via structure-based virtual screening (VS) with consensus scoring approach. Seven compounds were identified with better IC50 values than acarbose (IC50  = 68.18 ± 1.01 µM), a well-known α-glucosidase inhibitor drug, which meant 35% success for our VS methodology. Compound 52, 54, 56, 59, and 81 proved highly potent with IC50 values in the range of 40-60 µM. According to the enzyme kinetics study, four of them were competitive, 56 was non-competitive inhibitor. Structure-activity relationships, quantum mechanical, and docking analyses showed that azole rings at ionized state may be key to the potency observed for the active compounds and modifications to shift the balance between the neutral and ionized states further to the latter could yield more potent derivatives.

Structure-based virtual screening, free energy of binding and molecular dynamics simulations to propose novel inhibitors of Mtb-MurB oxidoreductase enzyme.

Currently, the growing incidence of drug resistance toward tuberculosis intensified the need for discovery of novel targets and their inhibitors. The enzyme MurB which is involved in one of the steps for peptidoglycan biosynthesis is an effective target that can produce drugs having lesser side-effects. Recently the only crystal structure of Mycobacterium Tuberculosis MurB has been deposited and, therefore, in the present study, we have used this as a target for virtual screening of drug-like molecules from the ZINC Database. We have also designed a complete workflow for the process which resulted in 12 hit compounds that have good docking scores, ΔGbind, and Glide energy. The hits obtained have also been found to share structural features with some known antibiotics such as Amoxicillin. Furthermore, MD simulations on the top most hit L1 displayed its stable binding with the enzyme. Thus, this study has proved helpful in proposing novel inhibitors for MurB enzyme that can be tested against various TB strains.

In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking.

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry.

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.