Computer-aided Drug Design Tools Research Articles

Two-Step Covalent Docking with Attracting Cavities.

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization.

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design.

With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.

Drug Likeness Screening and Evaluation of Physicochemical Properties of Selected Medicinal Agents by Computer Aided Drug Design Tools

The current drug research techniques, combinatorial synthesis and high throughput screening, help in obtaining and pre-evaluating thousands of compounds in a short duration of time. Among the selected compounds, the best hit molecule becomes the lead, and the drug likeness property helps to optimize the selection. Lipinski's Rule of five is the most widely used filter in pre-evaluation of compounds, according to Lipinski's Rule of five the molecule with poor oral absorption and low permeation has the molecular weight >500, ClogP >5, hydrogen bond acceptor more than 10 and hydrogen bond donor more than 5. To evaluate the Rule of Five, the top selling drugs of 2018-19 were selected. Among the selected 56 drugs, 12 drugs (tenofovir disoproxil, sofosbuvir and ledipasvir, sofosbuvir/velpatasvir, trastuzumab, glecaprevir, pegtilgrastion, Lumacaftor/ ivacaftor, fluticasone propionate and salmetarol, elbasvir, darunavir ethanolate, Linagliptin, metformin, fluticasone furoate and vilanterol) did not fulfill the rule. The rule is very useful in selecting the better compound in chemolibraries, but should be used carefully and with certain criterion due to possible exclusion of promising compounds.

Calendula officinalis Phytochemicals for the Treatment of Wounds Through Matrix Metalloproteinases-8 and 9 (MMP-8 and MMP-9): In Silico Approach

Diabetic foot ulceration is a common complication of an uncontrolled diabetic regimen and is considered a serious type of wound. Matrix metalloprteinases (MMPs) are the common key enzymes in wound management, overexpression of MMPs can lead to chronic wounds and ulcers. Calendula officinalis extract has established its efficacy in treating wounds in folk medicine. In this research work, we will focus on the chemical constituents of this promising herb and will investigate its abilities to target matrix metalloprteinase-8 (MMP-8) and matrix metalloprteinase-9 (MMP-9) proteins through the usage of computer-aided drug design tools. In the current study, several promising dual inhibitors are identified, such as quercetin, isoquercetin, isorhamnetin, and isorhamnetin 3-O glycoside, they showed to be good inhibitors for both enzyme subtypes with greater docking score energies than RND-336, which has been reported as a selective MMP-9 inhibitor. Binding scores and amino acid interactions in addition to molecular dynamics (MD) will be discussed in detail through this research work.

Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11β-HSD1 Inhibitors.

The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4-triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P21/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO2 and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO2/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO2 group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11β-HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11β-HSD1 inhibitors were able to accurately dock within the 11β-HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11β-HSD1 binding affinity scores as well as interactions to a known potent 11β-HSD1 inhibitor.

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents.

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents. To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction. Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Computational techniques in designing a series of 1,3,4-trisubstituted pyrazoles as unique hepatitis C virus entry inhibitors

In this study, we developed a QSAR model for studying the antiviral activity of 1,3,4-trisubstituted pyrazoles derivatives on hepatitis C virus infected in human HuH-7 cell lines. We employed random analysis to split the data sets. Statistically robust model was generated with R2, Q2 and R2pred values of 0.777, 0.731 and 0.774 respectively. The reliability of this model was confirmed by acceptable validation parameters, and this model also fulfilled the Golbraikh and Tropsha standard model conditions. Through the evaluation of selected molecular descriptors we observed that, topological charge index of order 4 (GGI4), mean topological charge index of order 1 (JGI1), octanol water partition coefficient (XlogP), 3D topological distance based autocorrelation lag5/weighted by polarizabilities (TDB5p) and total molecular surface area (FPSA-2) are the molecular properties determining biological activities of the study compounds, which shed light on the vital features that aid in the design of unique potent hepatitis C virus entry inhibitors using computer-aided drug design tools.

Structure Based Drug Design Approach to Identify Potential SARS-CoV-2 Polymerase Inhibitors

Aims: The research work aims to apply the current virtual screening approaches for rapid screening of available compounds as inhibitors of the novel coronavirus (COVID-19). Background: The worldwide pandemic, uncontrolled spread, and lack of effective therapeutics demand novel SARS-CoV-2 inhibitory anti-viral agents. Objective: The major objectives of the present work are – i) effective utilization of open-source computer- aided drug design (CADD) tools; ii) to prepare a database according to chemical structure similarity to the reported anti-viral drug, Favipiravir; and iii) to investigate potential inhibitors of the novel coronavirus. Methods: The dataset was prepared based on the chemical structure similarity feature of ChemSpider. The virtual screening was carried out using molecular docking and ADMET properties. For performing molecular docking studies, the standard docking protocol of iGEMDOCK was used. Result: Based on chemical structure similarity search to Favipiravir, a small library of 40 compounds was designed. The docking score and ADMET properties were analyzed to prioritize the compounds. Conclusion: The virtual screening resulted in the identification of potential anti-viral compounds. Among the designed library of compounds based on structural similarity to Favipiravir, 70% of compounds were found to possess docking scores more than that of Favipiravir. The amino acid residues involved in binding at the RNA dependent RNA polymerase (RdRp) were identified. The compounds have shown acceptable ADME properties and are potentially non-toxic. Other: The study has successfully applied the open source CADD tools to investigate novel SARS-CoV-2 polymerase inhibitors.

Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents

Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Natural Product Chemistry and Computer Aided Drug Design an Approach to Drug Discovery: A Review Article

Natural products have been an inherent part of sustaining acculturation because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity and accidental experience, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. However there has been a great breakthrough in the study of computer aided drug design (CADD) as many unfruitful lab researches have been averted and money, time and energies saved through CADD. Computer-aided drug design is a stimulating, arousing and manifold discipline where various aspects of applied and basic research integrate and induce each other. The empirical basis of CADD involves quantum mechanics and molecular modeling studies like structure based drug design; ligand-based drug design; database searching and binding affinity based on the knowledge of a biological target. In this present review we present the areas where natural product chemistry and CADD tools support drug discovery processes.

SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules.

SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15–20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch).

In Silico Exploration of Aryl Halides Analogues as CheckpointKinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study,and ADMET Screening

Purpose: In this review, a set of aryl halides analogs were identified as potent checkpoint kinase 1 (Chk1) inhibitors through a series of computer-aided drug design processes, to develop models with good predictive ability, highlight the important interactions between the ligand and the Chk1 receptor protein and determine properties of the new proposed drugs as Chk1 inhibitors agents. Methods: Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET) approaches are used to determine structure activity relationship and confirm the stable conformation on the receptor pocket. Results: The statistical analysis results of comparative -molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models that employed for a training set of 24 compounds gives reliable values of Q2 (0.70 and 0.94, respectively) and R2 (0.68 and 0.96, respectively). Conclusion: Computer–aided drug design tools used to develop models that possess good predictive ability, and to determine the stability of the observed and predicted molecules in the receptor pocket, also in silico of pharmacokinetic (ADMET) results shows good properties and bioavailability for these new proposed Chk1 inhibitors agents.

Exploring the Molecular Mechanism of Action Between Drug and RNA Polymerase Based on Partially-Resolved Spatial Structures

The RNA polymerase of Influenza A virus (IAV), which is comprised of three units PA, PB1 and PB2, is involved in transcription and replication of the influenza virus. In order to develop effective treatment for IAV, researchers have focused on designing drugs targeting IAV polymerase. Currently, crystal structures of the IAV polymerase PA-PB1, PB1-PB2 complexes and the PA subunit have been obtained by several groups, providing useful information regarding potential binding sites in drug design. However, to gain full understanding of the molecular mechanism of IAV polymerase in viral transcription and replication, thereby aiding drug development, a complete atomistic structure of the RNA polymerase is required. In this paper, we employed computer-aided drug design tools to describe the complete structure of the RNA polymerase and proposed a putative mechanism. We predict that the combination of Vancomycin and Oseltamivir will be an effective drug to universally treat IAVs with no resultant drug resistance if this putative mechanism is true.

Computer tools in the discovery of HIV-1 integrase inhibitors.

Computer-aided drug design (CADD) methodologies have made great advances and contributed significantly to the discovery and/or optimization of many clinically used drugs in recent years. CADD tools have likewise been applied to the discovery of inhibitors of HIV-1 integrase, a difficult and worthwhile target for the development of efficient anti-HIV drugs. This article reviews the application of CADD tools, including pharmacophore search, quantitative structure-activity relationships, model building of integrase complexed with viral DNA and quantum-chemical studies in the discovery of HIV-1 integrase inhibitors. Different structurally diverse integrase inhibitors have been identified by, or with significant help from, various CADD tools.

Rational design of CCR2 antagonists: a survey of computational studies

Importance of the field: CC-chemokine receptor 2 (CCR2) belongs to the GPCR superfamily and is the primary receptor for monocyte chemoattractant protein-1 (MCP-1), also known as chemokine ligand CCL2. Studies indicate the possible involvement of MCP-1 and CCR2 in various disease conditions, such as rheumatic arthritis, multiple sclerosis, vascular diseases, obesity and diabetes, via the inflammatory pathway. MCP-1 and CCR2 knockout mice under a broad range of stimuli exhibit deficient monocyte recruitment suggesting its potential role in inflammation. Overall, there is evidence that an impairment of monocyte trafficking in inflammation models occurs when there is a loss of MCP-1 effector function. This makes its receptor, CCR2, an attractive target for pharmaceutical research. Several small molecular CCR2 antagonists have been developed, particularly in the industry.Areas covered in this review: In this article, we have summarized the in silico work carried out in the area of CCR2 and reviewed mainly the computer aided drug design (CADD) studies reported on quantitative structure–activity relationship, homology modeling, molecular docking and virtual screening.What the reader will gain: A survey of computational studies for the rational design and development of CCR2 antagonists.Take home message: CADD tools can be used to rationalize the identification of the potential leads and these techniques can be effectively applied in the rapid searching of novel and potent CCR2 antagonists.

Binding Mechanism of H5N1 Influenza Virus Neuraminidase with Ligands and its Implication for Drug Design

To simulate new strategies for designing effective drugs against bird flu, we have carried out extensive studies by using various computer-aided drug design tools. Molecule AG7088 was first docked to the active site of H5N1 avian influenza neuraminidase (PBD code: 2HTY). The results thus obtained were compared with those by docking zanamivir (Relenza) and oseltamivir (Tamiflu) to the same receptor, respectively. It has been found that the compound AG7088 has better binding energy than zanamivir and oseltamivir. Thus, it was adopted as a template to perform the similarity search of 392,698 druggable compounds in order to find the leading candidates for the next step of modeling studies. Nine analogs of AG7088 were singled out through a series of docking studies. Finally, the molecular dynamics simulation technique was utilized to investigate into the binding interactions between the H5N1 receptor and the nine analogs, with a focus on the binding pocket, intermolecular surfaces and hydrogen bonds. This study may be used as a guide for mutagenesis studies for designing new inhibitors against H5N1.

A General Method for Exploiting QSAR Models in Lead Optimization

Computer-aided drug design tools can generate many useful and powerful models that explain structure-activity relationship (SAR) observations in a quantitative manner. These models can use many different descriptors, functional forms, and methods from simple linear equations through to multilayer neural nets. Using a model, a medicinal chemist can compute an activity, given a structure, but it is much harder to work out what changes are needed to make a structure more active. The impact of a model on the design process would be greatly enhanced if the model were more interpretable to the bench chemist. This paper describes a new protocol for performing automated iterative quantitative structure-activity relationship (QSAR) studies and presents the results of experiments on two QSAR sets from the literature. The fundamental goal of this work is to try to assist the chemist in his search for what to make next.