Computer-aided Drug Discovery Approach Research Articles

Computer-Aided Drug Discovery Approaches in the Identification of Natural Products against SARS-CoV-2: A Review.

The COVID-19 pandemic is raising a worldwide search for compounds that could act against the disease, mainly due to its mortality. With this objective, many researchers invested in the discovery and development of drugs of natural origin. To assist in this search, the potential of computational tools to reduce the time and cost of the entire process is known. Thus, this review aimed to identify how these tools have helped in the identification of natural products against SARS-CoV-2. For this purpose, a literature review was carried out with scientific articles with this proposal where it was possible to observe that different classes of primary and, mainly, secondary metabolites were evaluated against different molecular targets, mostly being enzymes and spike, using computational techniques, with emphasis on the use of molecular docking. However, it is noted that in silico evaluations still have much to contribute to the identification of an anti-SARS-CoV-2 substance, due to the vast chemical diversity of natural products, identification and use of different molecular targets and computational advancement.

Computer-Aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review.

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Identification of natural antiviral drug candidates against Tilapia Lake Virus: Computational drug design approaches.

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.

Identification of potential biogenic chalcones against antibiotic resistant efflux pump (AcrB) via computational study

The cases of bacterial multidrug resistance are increasing every year and becoming a serious concern for human health. Multidrug efflux pumps are key players in the formation of antibiotic resistance, which transfer out a broad spectrum of drugs from the cell and convey resistance to the host. Efflux pumps have significantly reduced the efficacy of the previously available antibiotic armory, thereby increasing the frequency of therapeutic failures. In gram-negative bacteria, the AcrAB-TolC efflux pump is the principal transporter of the substrate and plays a major role in the formation of antibiotic resistance. In the current work, advanced computer-aided drug discovery approaches were utilized to find hit molecules from the library of biogenic chalcones against the bacterial AcrB efflux pump. The results of the performed computational studies via molecular docking, drug-likeness prediction, pharmacokinetic profiling, pharmacophore mapping, density functional theory, and molecular dynamics simulation study provided ZINC000004695648, ZINC000014762506, ZINC000014762510, ZINC000095099506, and ZINC000085510993 as stable hit molecules against the AcrB efflux pumps. Identified hits could successfully act against AcrB efflux pumps after optimization as lead molecules. Communicated by Ramaswamy H. Sarma

Anticancer derivative of the natural alkaloid, theobromine, inhibiting EGFR protein: Computer-aided drug discovery approach.

A new semisynthetic derivative of the natural alkaloid, theobromine, has been designed as a lead antiangiogenic compound targeting the EGFR protein. The designed compound is an (m-tolyl)acetamide theobromine derivative, (T-1-MTA). Molecular Docking studies have shown a great potential for T-1-MTA to bind to EGFR. MD studies (100 ns) verified the proposed binding. By MM-GBSA analysis, the exact binding with optimal energy of T-1-MTA was also identified. Then, DFT calculations were performed to identify the stability, reactivity, electrostatic potential, and total electron density of T-1-MTA. Furthermore, ADMET analysis indicated the T-1-MTA's general likeness and safety. Accordingly, T-1-MTA has been synthesized to be examined in vitro. Intriguingly, T-1-MTA inhibited the EGFR protein with an IC50 value of 22.89 nM and demonstrated cytotoxic activities against the two cancer cell lines, A549, and HCT-116, with IC50 values of 22.49, and 24.97 μM, respectively. Interestingly, T-1-MTA's IC50 against the normal cell lines, WI-38, was very high (55.14 μM) indicating high selectivity degrees of 2.4 and 2.2, respectively. Furthermore, the flow cytometry analysis of A549 treated with T-1-MTA showed significantly increased ratios of early apoptosis (from 0.07% to 21.24%) as well as late apoptosis (from 0.73% to 37.97%).

The discovery of some promising putative binders of KRAS G12D receptor using computer-aided drug discovery approach

A prospective target for the therapy of solid tumors is KRAS G12D, the most prevalent oncogenic KRAS mutation. However, despite decades of research that focused on the identification of druggable compounds against this highly challenging therapeutic cancer target, no drug has been clinically approved for the treatment of KRAS G12D-driven cancers. Herein, we employed computational techniques such as molecular docking and molecular dynamics simulations to investigate small-molecule compounds with the potential to bind putatively with KRAS G12D. The docking screening portrayed three compounds (Quercetin, Psoralidin, and Resveratrol) as promising drug candidates for the receptor target due to their higher binding affinities when compared with a known noncovalent, potent, and selective KRAS G12D inhibitor (MRTX1133). The stability analysis after 100000 ps molecular dynamics simulation suggests Quercetin as a more stable compound when compared with the other simulated chemical entities, including the referenced inhibitor (MRTX1133). In spite of our research findings, it will be too early to conclude the drug candidates can be advanced to the clinic for use with KRAS G12D cancer patients without extensive preclinical and clinical studies.

Identification of a potent dual-function inhibitor for hIMPDH isoforms by computer-aided drug discovery approaches.

Inosine monophosphate dehydrogenase (IMPDH) is a key enzyme in de novo biosynthesis of purine nucleotides. Due to this important role, it is a great target to drug discovery for a wide range of activities, especially immunosuppressant in heart and kidney transplantation. Both human IMPDH isoforms are expressed in stimulated lymphocytes. In addition to the side effects of existing drugs, previous studies have mainly focused on the type II isoform. In this study, virtual screening and computer-aided approaches were employed to identify potential drugs with simultaneous inhibitory effects on both human IMPDH isoforms. After Re-docking, Double-step docking, and identification of virtual hits based on the PLANTS scoring function, drug-likeness and ADME-Tox assessments of the topmost ligands were performed. Following further evaluation, the best ligand was selected and, in complex with both isoforms, simulated in monomeric and tetrameric forms using molecular dynamics to evaluate its stability and binding pattern. The results showed a potential drug candidate [(S)-N-(3-hydroxy-1-(4-hydroxyphenyl) propyl)-2-(3-methyl-2,4-dioxo-3,4-dihydropyrimidin-1(2H)-yl) acetamide] with a high inhibitory effect on the two human IMPDH isoforms. This drug-like inhibitor could potentially serve as an immunosuppressant to prevent transplant rejection response by inhibiting B- and T-lymphocyte proliferation. In addition, its effect can be evaluated in various therapeutic targets in which IMPDH is known as a therapeutic target, especially in Covid-19 patients.

Withaniasomnifera phytochemicals possess SARS-CoV-2 RdRp and human TMPRSS2 protein binding potential.

Coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has infected approximately 26 million people and caused more than 6 million deaths globally. Spike (S)-protein on the outer surface of the virus uses human trans-membrane serine protease-2 (TMPRSS2) to gain entry into the cell. Recent reports indicate that human dipeptidyl peptidase-4 inhibitors (DPP4 or CD26) could also be utilized to check the S-protein mediated viral entry into COVID-19 patients. RNA dependent RNA polymerase (RdRp) is another key virulence protein of SARS-CoV-2 life cycle. The study aimed to identify the potential anti-SARS-CoV-2 inhibitors present in Withania somnifera (Solanaceae) using computer aided drug discovery approach. Molecular docking results showed that flavone glycoside, sugar alcohol, and flavonoid present in W. somnifera showed − 11.69, − 11.61, − 10.1, − 7.71 kcal/mole binding potential against S-protein, CD26, RdRp, and TMPRSS2 proteins. The major standard inhibitors of the targeted proteins (Sitagliptin, VE607, Camostat mesylate, and Remdesivir) showed the − 7.181, − 6.6, − 5.146, and − 7.56 kcal/mole binding potential. Furthermore, the lead phytochemicals and standard inhibitors bound and non-bound RdRp and TMPRSS2 proteins were subjected to molecular dynamics (MD) simulation to study the complex stability and change in protein conformation. The result showed energetically favorable and stable complex formation in terms of RMSD, RMSF, SASA, Rg, and hydrogen bond formation. Drug likeness and physiochemical properties of the test compounds exhibited satisfactory results. Taken together, the present study suggests the presence of potential anti-SARS-CoV-2 phytochemicals in W. somnifera that requires further validation in in vitro and in vivo studies.Graphical Supplementary informationThe online version contains supplementary material available at 10.1007/s42535-022-00404-4.

Design, Synthesis, and Action Mechanism of 1,3-Benzodioxole Derivatives as Potent Auxin Receptor Agonists and Root Growth Promoters

Deeper and longer roots allow crops to survive and flourish, but our understanding of the plant growth regulators promoting root system establishment is limited. Here, we report that, a novel auxin receptor agonist, named K-10, had a remarkable promotive effect on root growth in both Arabidopsis thaliana and Oryza sativa through the enhancement of root-related signaling responses. Using computer-aided drug discovery approaches, we developed potent lead compound by screening artificial chemicals on the basis of the auxin receptor TIR1 (Transport Inhibitor Response 1), and a series of N-(benzo[d] [1,3] dioxol-5-yl)-2-(one-benzylthio) acetamides, K-1 to K-22, were designed and synthesized. The results of bioassay showed that K-10 exhibited an excellent root growth-promoting activity far exceeding that of NAA (1-naphthylacetic acid). A further morphological investigation of the auxin related mutants (yucQ, tir1) revealed that K-10 had auxin-like physiological functions and was recognized by TIR1, and K-10 significantly enhanced auxin response reporter’s (DR5:GUS) transcriptional activity. Consistently, transcriptome analysis showed that K-10 induced a common transcriptional response with auxin and down-regulated the expression of root growth-inhibiting genes. Further molecular docking analysis revealed that K-10 had a stronger binding ability with TIR1 than NAA. These results indicated that this class of derivatives could be a promising scaffold for the discovery and development of novel auxin receptor agonists, and the employment of K-10 may be effective for enhancing root growth and crop production.

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years.

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Molecular Docking Identifies Novel Phytochemical Inhibitors Against SARS-COV-2 for Covid-19 Therapy

SARS-CoV-2 the new strain of SARS corona virus is an RNA virus that inflicts acute respiratory distress syndrome due to infection of the alveolar epithelial cells, its primary target. No effective drug is currently available to treat this viral infection. Therefore, we focused on identifying inhibitors of the main viral protease domain (Mpro) which plays important role in the virus life cycle. Two tired computer-aided drug discovery approach were adopted for screening of novel inhibitors against Mpro, the target protein. First, based on their ADME/T properties, phytochemicals as well as synthetic drugs six compounds were selected from the available database. In second screening by molecular docking based on binding affinity and molecular interactions of these compounds with Mpro led to the identification of the best phytochemical and synthetic compound against Mpro. The result of docking complex showed that, interacting residues for myricetin are continuous while, in case of fosamprenavir, these are non-contiguous. Both molecules interact with the residues in the active site occupying the site for the catalytic activity indicate possible competitive inhibitors of the Mpro.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches.

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson–Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain.

Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.

Inhibition of jack bean urease by amphiphilic peptides

In the current study, amphiphilic peptides were designed and screened against Jack bean urease by using computer aided drug discovery approach. The result showed that out of thirty-eight amphiphilic peptides 1, 3, 12, 18, 30, and 33 exhibit stronger binding affinity with the active site of the enzyme through chelation of charged amino acids with the nickel ions i.e., Ni+2 841 and Ni+2 842 as well as hydrophobic contacts of the nonpolar tail with the nonpolar residues in the active site. The selected amphiphilic peptides were synthesized by solid-phase peptide synthesis strategy, characterized by fast atomic bombardment mass spectroscopy (FAB-MS) and nuclear magnetic resonance spectroscopy (1H and 13C-NMR) and in vitro urease inhibitory activity of amphiphilic peptides was studied. Amphiphilic peptides 12 and 33 showed excellent urease inhibitory activity, (p < 0.001) with IC50 values 20.5 ± 0.01, and 28.1 ± 0.03 µM respectively, which was considerably better than thiourea used as positive control.

Dual-Inhibitors of N-Myc and AURKA as Potential Therapy for Neuroendocrine Prostate Cancer.

Resistance to androgen-receptor (AR) directed therapies is, among other factors, associated with Myc transcription factors that are involved in development and progression of many cancers. Overexpression of N-Myc protein in prostate cancer (PCa) leads to its transformation to advanced neuroendocrine prostate cancer (NEPC) that currently has no approved treatments. N-Myc has a short half-life but acts as an NEPC stimulator when it is stabilized by forming a protective complex with Aurora A kinase (AURKA). Therefore, dual-inhibition of N-Myc and AURKA would be an attractive therapeutic avenue for NEPC. Following our computer-aided drug discovery approach, compounds exhibiting potent N-Myc specific inhibition and strong anti-proliferative activity against several N-Myc driven cell lines, were identified. Thereafter, we have developed dual inhibitors of N-Myc and AURKA through structure-based drug design approach by merging our novel N-Myc specific chemical scaffolds with fragments of known AURKA inhibitors. Favorable binding modes of the designed compounds to both N-Myc and AURKA target sites have been predicted by docking. A promising lead compound, 70812, demonstrated low-micromolar potency against both N-Myc and AURKA in vitro assays and effectively suppressed NEPC cell growth.

Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

ABSTRACT Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

Computer-aided Discovery of a New Nav1.7 Inhibitor for Treatment of Pain and Itch.

Voltage-gated sodium channel Nav1.7 has been validated as a perspective target for selective inhibitors with analgesic and anti-itch activity. The objective of this study was to discover new candidate compounds with Nav1.7 inhibitor properties. The authors hypothesized that their approach would yield at least one new compound that inhibits sodium currents in vitro and exerts analgesic and anti-itch effects in mice. In silico structure-based similarity search of 1.5 million compounds followed by docking to the Nav1.7 voltage sensor of Domain 4 and molecular dynamics simulation was performed. Patch clamp experiments in Nav1.7-expressing human embryonic kidney 293 cells and in mouse and human dorsal root ganglion neurons were conducted to test sodium current inhibition. Formalin-induced inflammatory pain model, paclitaxel-induced neuropathic pain model, histamine-induced itch model, and mouse lymphoma model of chronic itch were used to confirm in vivo activity of the selected compound. After in silico screening, nine compounds were selected for experimental assessment in vitro. Of those, four compounds inhibited sodium currents in Nav1.7-expressing human embryonic kidney 293 cells by 29% or greater (P < 0.05). Compound 9 (3-(1-benzyl-1H-indol-3-yl)-3-(3-phenoxyphenyl)-N-(2-(pyrrolidin-1-yl)ethyl)propanamide, referred to as DA-0218) reduced sodium current by 80% with a 50% inhibition concentration of 0.74 μM (95% CI, 0.35 to 1.56 μM), but had no effects on Nav1.5-expressing human embryonic kidney 293 cells. In mouse and human dorsal root ganglion neurons, DA-0218 reduced sodium currents by 17% (95% CI, 6 to 28%) and 22% (95% CI, 9 to 35%), respectively. The inhibition was greatly potentiated in paclitaxel-treated mouse neurons. Intraperitoneal and intrathecal administration of the compound reduced formalin-induced phase II inflammatory pain behavior in mice by 76% (95% CI, 48 to 100%) and 80% (95% CI, 68 to 92%), respectively. Intrathecal administration of DA-0218 produced acute reduction in paclitaxel-induced mechanical allodynia, and inhibited histamine-induced acute itch and lymphoma-induced chronic itch. This study's computer-aided drug discovery approach yielded a new Nav1.7 inhibitor that shows analgesic and anti-pruritic activity in mouse models.

TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches.

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

AntiHIV-Pred: web-resource for in silico prediction of anti-HIV/AIDS activity.

Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time and financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules. Over 50000 experimental records for anti-retroviral agents from ChEMBL database were extracted for creating the training sets. After careful examination, about seven thousand molecules inhibiting five HIV-1 proteins were used to develop regression and classification models with the GUSAR software. The average values of R2 = 0.95 and Q2 = 0.72 in validation procedure demonstrated the reasonable accuracy and predictivity of the obtained (Q)SAR models. Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program. Freely available on the web at http://www.way2drug.com/hiv/. Supplementary data are available at Bioinformatics online.

Computer-Aided Discovery of Small Molecules Targeting the RNA Splicing Activity of hnRNP A1 in Castration-Resistant Prostate Cancer.

The heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a versatile RNA-binding protein playing a critical role in alternative pre-mRNA splicing regulation in cancer. Emerging data have implicated hnRNP A1 as a central player in a splicing regulatory circuit involving its direct transcriptional control by c-Myc oncoprotein and the production of the constitutively active ligand-independent alternative splice variant of androgen receptor, AR-V7, which promotes castration-resistant prostate cancer (CRPC). As there is an urgent need for effective CRPC drugs, targeting hnRNP A1 could, therefore, serve a dual purpose of preventing AR-V7 generation as well as reducing c-Myc transcriptional output. Herein, we report compound VPC-80051 as the first small molecule inhibitor of hnRNP A1 splicing activity discovered to date by using a computer-aided drug discovery approach. The inhibitor was developed to target the RNA-binding domain (RBD) of hnRNP A1. Further experimental evaluation demonstrated that VPC-80051 interacts directly with hnRNP A1 RBD and reduces AR-V7 messenger levels in 22Rv1 CRPC cell line. This study lays the groundwork for future structure-based development of more potent and selective small molecule inhibitors of hnRNP A1–RNA interactions aimed at altering the production of cancer-specific alternative splice isoforms.