Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a-h), and hydrazine monohydrate (NH2NH2•H2O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a-h). After synthesis and characterization of the mentioned cinnolines (3a-h), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, and hDHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC50, EC50, etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds (3a-h). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines (3a-h), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

DFT, molecular docking and molecular dynamics simulation studies on some recent natural products revealing their EGFR tyrosine kinase inhibition potential

Phytochemicals are important chemical compounds in pharmaceutical chemistry. These natural compounds have interesting biological activities, including anticancer, as well as many other functions. EGFR (epidermal growth factor receptor) tyrosine kinase inhibition is emerging as one of the accepted methods in the treatment of cancer. On the other hand, computer-aided drug design has become an increasingly important field of study due to its many important advantages such as efficient use of time and other resources. In this study, fourteen phytochemicals which have triterpenoid structure and have recently entered the literature were investigated computationally for their potential as EGFR tyrosine kinase inhibitors. In the study, DFT (density functional theory) calculations, molecular docking, molecular dynamics simulations, binding free energy calculations with the use of MM-PBSA (molecular mechanics Poisson-Boltzmann Surface Area) method, and ADMET predictions were performed. The obtained results were compared to the results obtained for reference drug Gefitinib. Results showed that the investigated natural compounds are promising structures for EGFR tyrosine kinase inhibition.Communicated by Ramaswamy H. Sarma.

Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea.

Lysine-Specific Demethylase 1 Inhibitors: A Comprehensive Review Utilizing Computer-Aided Drug Design Technologies

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.

Targeted Drug Designing for Treating Masticatory Myofascial Pain Dysfunction Syndrome: An In Silico Simulation Study

Background Masticatory Myofascial Pain Dysfunction Syndrome (MMPDS) is a musculoligamentous disorder that shares similarities with temporomandibular joint pain and odontogenic pain. It manifests as dull or aching pain in masticatory muscles, influenced by jaw movement. Computer-aided drug design (CADD) encompasses various theoretical and computational approaches used in modern drug discovery. Molecular docking is a prominent method in CADD that facilitates the understanding of drug-bimolecular interactions for rational drug design, mechanistic studies & the formation of stable complexes with increased specificity and potential efficacy. The docking technique provides valuable insights into binding energy, free energy, and complex stability predictions. Aim The aim of this study was to use the docking technique for myosin inhibitors. Materials and methods Four inhibitors of myosin were chosen from the literature. These compound structures were retrieved from the Zinc15 database. Myosin protein was chosen as the target and was optimized using the RCSB Protein Data Bank. After pharmacophore modeling, 20 novel compounds were found and the SwissDock was used to dock them with the target protein. We compared the binding energies of the newly discovered compounds to those of the previously published molecules with the target. Results The results indicated that among the 20 molecules ZINC035924607 and ZINC5110352 exhibited the highest binding energy and displayed superior properties compared to the other molecules. Conclusion The study concluded that ZINC035924607 and ZINC5110352 exhibited greater binding affinity than the reported inhibitors of myosin. Therefore, these two molecules can be used as a potential and promising lead for the treatment of MMPDS and could be employed in targeted drug therapy.

Dual Targeting of DNA Damage Response Proteins Implicated in Cancer Radioresistance

Ionizing radiation can induce different types of DNA lesions, leading to genomic instability and ultimately cell death. Radiation therapy or radiotherapy, a major modality in cancer treatment, harnesses the genotoxic potential of radiation to target and destroy cancer cells. Nevertheless, cancer cells have the capacity to develop resistance to radiation treatment (radioresistance), which poses a major obstacle in the effective management of cancer. It has been shown that administration of platinum-based drugs to cancer patients can increase tumor radiosensitivity, but despite this, it is associated with severe adverse effects. Several lines of evidence support that activation of the DNA damage response and repair machinery in the irradiated cancer cells enhances radioresistance and cellular survival through the efficient repair of DNA lesions. Therefore, targeting of key DNA damage repair factors would render cancer cells vulnerable to the irradiation effects, increase cancer cell killing, and reduce the risk of side effects on healthy tissue. Herein, we have employed a computer-aided drug design approach for generating ab initio a chemical compound with drug-like properties potentially targeting two proteins implicated in multiple DNA repair pathways. The findings of this study could be taken into consideration in clinical decision-making in terms of co-administering radiation with DNA damage repair factor-based drugs.

A novel class of broad-spectrum active-site-directed 3C-like protease inhibitors with nanomolar antiviral activity against highly immune-evasive SARS-CoV-2 Omicron subvariants

Antivirals with broad coronavirus activity are important for treating high-risk individuals exposed to the constantly evolving SARS-CoV-2 variants of concern (VOCs) as well as emerging drug-resistant variants. We developed and characterized a novel class of active-site-directed 3-chymotrypsin-like protease (3CLpro) inhibitors (C2-C5a). Our lead direct-acting antiviral (DAA), C5a, is a non-covalent, non-peptide with a dissociation constant of 170 nM against recombinant SARS-CoV-2 3CLpro. The compounds C2-C5a exhibit broad-spectrum activity against Omicron subvariants (BA.5, BQ.1.1, and XBB.1.5) and seasonal human coronavirus-229E infection in human cells. Notably, C5a has median effective concentrations of 30-50 nM against BQ.1.1 and XBB.1.5 in two different human cell lines. X-ray crystallography has confirmed the unique binding modes of C2-C5a to the 3CLpro, which can limit virus cross-resistance to emerging Paxlovid-resistant variants. We tested the effect of C5a with two of our newly discovered host-directed antivirals (HDAs): N-0385, a TMPRSS2 inhibitor, and bafilomycin D (BafD), a human vacuolar H+-ATPase [V-ATPase] inhibitor. We demonstrated a synergistic action of C5a in combination with N-0385 and BafD against Omicron BA.5 infection in human Calu-3 lung cells. Our findings underscore that a SARS-CoV-2 multi-targeted treatment for circulating Omicron subvariants based on DAAs (C5a) and HDAs (N-0385 or BafD) can lead to therapeutic benefits by enhancing treatment efficacy. Furthermore, the high-resolution structures of SARS-CoV-2 3CLpro in complex with C2-C5a will facilitate future rational optimization of our novel broad-spectrum active-site-directed 3C-like protease inhibitors.

Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules

Virtual small molecule libraries are valuable resources for identifying bioactive compounds in virtual screening campaigns and improving the quality of libraries in terms of physicochemical properties, complexity, and structural diversity. In this context, the computational-aided design of libraries focused against antidiabetic targets can provide novel alternatives for treating type II diabetes mellitus (T2DM). In this work, we integrated the information generated to date on compounds with antidiabetic activity, advances in computational methods, and knowledge of chemical transformations available in the literature to design multi-target compound libraries focused on T2DM. We evaluated the novelty and diversity of the newly generated library by comparing it with antidiabetic compounds approved for clinical use, natural products, and multi-target compounds tested in vivo in experimental antidiabetic models. The designed libraries are freely available and are a valuable starting point for drug design, chemical synthesis, and biological evaluation or further computational filtering. Also, the compendium of 280 transformation rules identified in a medicinal chemistry context is made available in the linear notation SMIRKS for use in other chemical library enumeration or hit optimization approaches.

Computer-aided drug design course for pharmacy major students in Shenyang Pharmaceutical University following the COVID-19 pandemic: Challenges and opportunities

The computer-aided drug design (CADD) course that spans biochemistry, computational chemistry, medicinal chemistry, and other cutting-edge sciences is considered an important course by pharmaceutical universities in China. The course teaches students how drugs bind to protein targets and exert their biological activities using computer tools, and covers the basic principles of drug development and optimization. Due to the lockdown and social distancing measures adopted during the coronavirus disease 2019 (COVID-19) pandemic, the CADD course in Shenyang Pharmaceutical University was briefly suspended. Thereafter, it was taught in the online mode by adopting a novel blended teaching method. Through a questionnaire survey and final report assessment, we found that blended teaching might provide an opportunity to stimulate greater motivation and interest in students as well as improve teaching effectiveness and learning outcomes of the course. This study describes how we conducted the CADD course during the COVID-19 period with the intention of providing a reference for other teachers to conduct similar courses.

Taxifolin as a Metallo-β-Lactamase Inhibitor in Combination with Augmentin against Verona Imipenemase 2 Expressing Pseudomonas aeruginosa

Among the various mechanisms that bacteria use to develop antibiotic resistance, the multiple expression of β-lactamases is particularly problematic, threatening public health and increasing patient mortality rates. Even if a combination therapy-in which a β-lactamase inhibitor is administered together with a β-lactam antibiotic-has proven effective against serine-β-lactamases, there are no currently approved metallo-β-lactamase inhibitors. Herein, we demonstrate that quercetin and its analogs are promising starting points for the further development of safe and effective metallo-β-lactamase inhibitors. Through a combined computational and in vitro approach, taxifolin was found to inhibit VIM-2 expressing P. aeruginosa cell proliferation at <4 μg/mL as part of a triple combination with amoxicillin and clavulanate. Furthermore, we tested this combination in mice with abrasive skin infections. Together, these results demonstrate that flavonol compounds, such as taxifolin, may be developed into effective metallo-β-lactamase inhibitors.

CoMFA Directed Molecular Design for Significantly Improving Fungicidal Activity of Novel [1,2,4]-Triazolo-[3,4-b][1,3,4]-thiadizoles

Target based molecular design via the aid of computation is one of the most efficient methods in the discovery of novel pesticides. Here, a combination of the comparative molecular field analysis (CoMFA) and molecular docking was applied for discovery of potent fungicidal [1,2,4]-triazolo-[3,4-b][1,3,4]-thiadiazoles. Bioassay results indicated that the synthesized target compounds 3a, 3b, and 3c exhibited good activity against Alternaria solani, Botrytis cinerea, Cercospora arachidicola, Fusarium graminearum, Physalospora piricola, Rhizoctonia solani, and Sclerotinia sclerotiorum with an EC50 value falling between 0.64 and 16.10 μg/mL. Specially, 3c displayed excellent fungicidal activity against C. arachidicola and R. solani, which was 5 times more potent than the lead YZK-C22. The enzymatic inhibition assay and fluorescence quenching analysis with R. solani pyruvate kinase (RsPK) showed a weaker binding affinity between RsPK and 3a, 3b, or 3c. Transcriptomic analyses showed that 3c exerted its fungicidal activity by disrupting steroid biosynthesis and ribosome biogenesis in eukaryotes. These findings support that 3c is a promising fungicide candidate, and a fine modification from a lead may lead to a totally different mode of action.

TransFoxMol: predicting molecular property with focused attention

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.

Exploring EGFR inhibitors with the aid of virtual screening, docking, and dynamics simulation studies

In humans, Epidermal Growth Factor Receptor (EGFR) is linked to small-cell lung cancer, breast cancer, and glioblastoma. Receptor kinase inhibitors against EGFR have become a standard treatment option for non-small cell lung cancer (NSCLC), breast cancer patients, and even for those with EGFR mutations or resistance. About 2734 FDA-approved medication compounds were subjected to virtual screening for EGFR kinase inhibitory activity. The top 30 molecules were chosen based on the binding affinity scores and subjected to extra-precision docking and binding free energy analysis. The ADMET profile of the top three hit molecules was verified to confirm their druggability nature. Top three hits- compound 1047 (ZINC000001550477), 1302 (ZINC00003781952), and 2332 (ZINC000019632618) were identified on account of their MMGBSA binding affinity values. The top three hit compounds were subjected to molecular dynamics (MD) simulation for 100 ns. The dynamic nature of the ligand-protein complex was analyzed which corroborated the results of molecular docking and MMGBSA analysis studies. All the top three hits were further subjected to steered MD studies for testing the strength of these ligand-receptor binding in the presence of an external force. Compound 2332 (ZINC000019632618) was identified as the best hit molecule that can be used as a lead to develop newer derivatives of EGFR kinase inhibitors.Communicated by Ramaswamy H. Sarma.

Discovery of Potential Noncovalent Inhibitors of Dehydroquinate Dehydratase from Methicillin-Resistant Staphylococcus aureus through Computational-Driven Drug Design

Bacteria resistance to antibiotics is a concerning global health problem; in this context, methicillin-resistant Staphylococcus aureus (MRSA) is considered as a high priority by the World Health Organization. Furthermore, patients with a positive result for COVID-19 received early antibiotic treatment, a fact that potentially encourages the increase in antibiotic resistance. Therefore, there is an urgency to develop new drugs with molecular mechanisms different from those of the actual treatments. In this context, enzymes from the shikimate pathway, a route absent in humans, such as dehydroquinate dehydratase (DHQD), are considered good targets. In this work, a computer-aided drug design strategy, which involved exhaustive virtual screening and molecular dynamics simulations with MM-PBSA analysis, as well as an in silico ADMETox characterization, was performed to find potential noncovalent inhibitors of DHQD from MRSA (SaDHQD). After filtering the 997 million compounds from the ZINC database, 6700 compounds were submitted to an exhaustive virtual screening protocol. From these data, four molecules were selected and characterized (ZINC000005753647 (1), ZINC000001720488 (2), ZINC000082049768 (3), and ZINC000644149506 (4)). The results indicate that the four potential inhibitors interacted with residues important for substrate binding and catalysis, with an estimated binding free energy like that of the enzyme's substrate. Their ADMETox-predicted properties suggest that all of them support the structural characteristics to be considered good candidates. Therefore, the four compounds reported here are excellent option to be considered for future in vitro studies to design new SaDHQD noncovalent inhibitors and contribute to the search for new drugs against MRSA.

A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein-protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs' biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments.Communicated by Ramaswamy H. Sarma.

Computational and experimental analyses of alanine racemase suggest new avenues for developing allosteric small-molecule antibiotics

Given the ever-present threat of antibacterial resistance, there is an urgent need to identify new antibacterial drugs and targets. One such target is alanine racemase (Alr), an enzyme required for bacterial cell-wall biosynthesis. Alr is an attractive drug target because it is essential for bacterial survival but is absent in humans. Existing drugs targeting Alr lack specificity and have severe side effects. We here investigate alternative mechanisms of Alr inhibition. Alr functions exclusively as an obligate homodimer, so we probed seven conserved interactions on the dimer interface, distant from the enzymatic active site, to identify possible allosteric influences on activity. Using the Alr from Mycobacterium tuberculosis (MT) as a model, we found that the Lys261/Asp135 salt bridge is critical for catalytic activity. The Lys261Ala mutation completely inactivated the enzyme, and the Asp135Ala mutation reduced catalytic activity eight-fold. Further investigation suggested a potential drug-binding site near the Lys261/Asp135 salt bridge that may be useful for allosteric drug discovery.

Targeting RNA Structure to Inhibit Editing in Trypanosomes

Mitochondrial RNA editing in trypanosomes represents an attractive target for developing safer and more efficient drugs for treating infections with trypanosomes because this RNA editing pathway is not found in humans. Other workers have targeted several enzymes in this editing system, but not the RNA. Here, we target a universal domain of the RNA editing substrate, which is the U-helix formed between the oligo-U tail of the guide RNA and the target mRNA. We selected a part of the U-helix that is rich in G-U wobble base pairs as the target site for the virtual screening of 262,000 compounds. After chemoinformatic filtering of the top 5000 leads, we subjected 50 representative complexes to 50 nanoseconds of molecular dynamics simulations. We identified 15 compounds that retained stable interactions in the deep groove of the U-helix. The microscale thermophoresis binding experiments on these five compounds show low-micromolar to nanomolar binding affinities. The UV melting studies show an increase in the melting temperatures of the U-helix upon binding by each compound. These five compounds can serve as leads for drug development and as research tools to probe the role of the RNA structure in trypanosomal RNA editing.

Advances of computer-aided drug design (CADD) in the development of anti-Azheimer's-disease drugs

Alzheimer's disease (AD) is a degenerative disease of the nervous system that progressively destroys memory and thinking skills. Currently there is no treatment to prevent or cure AD; targeting the direct cause of neuronal degeneration would constitute a rational strategy and hopefully offer better options for the treatment of AD. This paper first summarizes the physiological and pathological pathogenesis of AD and then discusses the representative drug candidates for targeted therapy of AD and their binding mode with their targets. Finally, the applications of computer-aided drug design in discovering anti-AD drugs are reviewed. Teaser.

Structure-based discovery of thiosemicarbazones as SARS-CoV-2 main protease inhibitors

Aim: Discovery of novel SARS-CoV-2 main protease (Mpro) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover Mpro inhibitors, which were subsequently evaluated in biochemical and cellular assays. Results: 91 virtual hits were selected for biochemical assays, and four were confirmed as reversible inhibitors of SARS CoV-2 Mpro with IC50 values of 0.4-3 μM. They were also shown to inhibit SARS-CoV-1 Mpro and human cathepsin L. Molecular dynamics simulations indicated the stability of the Mpro inhibitor complexes and the interaction of ligands at the subsites. Conclusion: This approach led to the discovery of novel thiosemicarbazones as potent SARS-CoV-2 Mpro inhibitors.

[Application and development of systems biology in computer-aided drug design]

With the advances in medicine, people have deeply understood the complex pathogenesis of diseases. Revealing the mechanism of action and therapeutic effect of drugs from an overall perspective has become the top priority of drug design. However, the traditional drug design methods cannot meet the current needs. In recent years, with the rapid development of systems biology, a variety of new technologies including metabolomics, genomics, and proteomics have been used in drug research and development. As a bridge between traditional pharmaceutical theory and modern science, computer-aided drug design(CADD) can shorten the drug development cycle and improve the success rate of drug design. The application of systems biology and CADD provides a methodological basis and direction for revealing the mechanism and action of drugs from an overall perspective. This paper introduces the research and application of systems biology in CADD from different perspectives and proposes the development direction, providing reference for promoting the application.

Computer-aided drug design approaches applied to screen natural product's structural analogs targeting arginase in Leishmania spp

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

The synthesis and effects of a novel TRPC6 inhibitor, BP3112, on hepatocellular carcinoma

Aims: Transient receptor potential canonical-6 (TRPC6) is a therapeutic target for hepatocellular carcinoma. The authors aimed to synthesize and determine whether indole-2-carboxamide derivatives have anti-hepatocellular carcinoma activities targeting TRPC6. Materials & methods: Molecular docking was carried out to design these derivatives. The top five compounds were synthesized for activity validation using microscale thermophoresis. Cell cytotoxicity, flow cytometry, western blotting and cell transfection were used to investigate the anti-hepatocellular carcinoma activities and mechanisms in vitro. Xenografts of nude mice were used for in vivo evaluation. Results: The indole-2-carboxamide derivative, BP3112, promoted apoptosis and G1-phase arrest in HCCs via inhibiting TRPC6, and dose-dependently inhibit tumor growth in vivo. Conclusion: BP3112 as a specific inhibitor of TRPC6 is a potential therapeutic agent for hepatocellular carcinoma.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μ_ex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μ_ex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μ_ex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

Exploring the Effects of Chirality of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl]imidazolidine-2,4-dione and its Derivatives on the Oncological Target Tankyrase 2. Atomistic Insights

BACKGROUND

Tankyrases (TNKS) are homomultimers existing in two forms, viz. TNKS1 and TNKS2. TNKS2 plays a pivotal role in carcinogenesis by activating the Wnt//β-catenin pathway. TNKS2 has been identified as a suitable target in oncology due to its crucial role in mediating tumour progression. The discovery of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione, a hydantoin phenylquinazolinone derivative which exists as a racemic mixture and in its pure enantiomer forms, has reportedly exhibited inhibitory potency towards TNKS2. However, the molecular events surrounding its chirality towards TNKS2 remain unresolved.

METHODS

Herein, we employed in silico methods such as molecular dynamics simulation coupled with binding free energy estimations to explore the mechanistic activity of the racemic inhibitor and its enantiomer forms on TNK2 at a molecular level Results: Favourable binding free energies were noted for all three ligands propelled by electrostatic and van der Waals forces. The positive enantiomer demonstrated the highest total binding free energy (-38.15 kcal/mol), exhibiting a more potent binding affinity to TNKS2. Amino acids PHE1035, ALA1038, and HIS1048; PHE1035, HIS1048 and ILE1039; and TYR1060, SER1033 and ILE1059 were identified as key drivers of TNKS2 inhibition for all three inhibitors, characterized by the contribution of highest residual energies and the formation of crucial high-affinity interactions with the bound inhibitors. Further assessment of chirality by the inhibitors revealed a stabilizing effect of the complex systems of all three inhibitors on the TNKS2 structure. Concerning flexibility and mobility, the racemic inhibitor and negative enantiomer revealed a more rigid structure when bound to TNKS2, which could potentiate biological activity interference. The positive enantiomer, however, displayed much more elasticity and flexibility when bound to TNKS2.

CONCLUSION

Overall, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione and its derivatives showed their inhibitory prowess when bound to the TNKS2 target via in silico assessment. Thus, results from this study offer insight into chirality and the possibility of adjustments of the enantiomer ratio to promote greater inhibitory results. These results could also offer insight into lead optimization to enhance inhibitory effects.

Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors

Ordinary small molecule de novo drug design is time-consuming and expensive. Recently, computational tools were employed and proved their efficacy in accelerating the overall drug design process. Molecular dynamics (MD) simulations and a derivative of MD, steered molecular dynamics (SMD), turned out to be promising rational drug design tools. In this paper, we report the first application of SMD to evaluate the binding properties of small molecules toward FABP4, considering our recent interest in inhibiting fatty acid binding protein 4 (FABP4). FABP4 inhibitors (FABP4is) are small molecules of therapeutic interest, and ongoing clinical studies indicate that they are promising for treating cancer and other diseases such as metabolic syndrome and diabetes.

Benchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design

Cryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 Å) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution-independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Computational Approaches to the Rational Design of Tubulin-Targeting Agents

Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.

Docking-based generative approaches in the search for new drug candidates

Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics

Developing a ligand with high affinity for a specific protein target is essential for drug design, and water molecules are well known to play a key role in protein-drug recognition. However, predicting the role of particularly ordered water molecules in drug binding remains challenging. Furthermore, hydration free energy contributed from the water network, including the second shell of water molecules, is far from being well studied. In this research we focused on these aspects to accurately and efficiently evaluate water effects in protein-ligand binding affinity. We developed a new strategy using a free-energy calculation method, VM2. We successfully predicted the stable ordered water molecules in a number of protein systems: PDE 10a, HSP90, tryptophan synthase (TRPS), CDK2 and Factor Xa. In some of these, the second shell of water molecules appeared to be critical in protein-ligand binding. We also applied the strategy to largely improve binding free energy calculation using the MM/PBSA method. When applying MM/PBSA alone for two systems, CDK2 and Factor Xa, the computed binding free energy resulted in poor to moderate R² values with experimental data. However, including water free energy correction greatly improved the free energy calculation. Furthermore, our work helped to explain how xk263 is a 1000 times faster binder to HIVp than ritonavir, a potentially useful tool for investigating binding kinetics. Our studies reveal the importance of fully considering water effects in therapeutic developments in pharmaceutical and biotechnology industries and for fundamental research in protein-ligand recognition.

Structure-Based Identification of Natural Compounds as Potential RET-Kinase Inhibitors for Therapeutic Targeting of Neurodegenerative Diseases

BACKGROUND

Tyrosine-protein kinase receptor Ret (RET), a proto-oncogene, is considered as an attractive drug target for cancer and neurodegenerative diseases, including Alzheimer's disease (AD).

OBJECTIVE

We aimed to identify potential inhibitors of RET kinase among natural compounds present in the ZINC database.

METHODS

A multistep structure-based virtual screening approach was used to identify potential RET kinase inhibitors based on their binding affinities, docking scores, and interactions with the biologically important residues of RET kinase. To further validate the potential of these compounds as therapeutic leads, molecular dynamics (MD) simulations for 100 ns were carried out and subsequently evaluated the stability, conformational changes, and interaction mechanism of RET in-complex with the elucidated compounds.

RESULTS

Two natural compounds, ZINC02092851 and ZINC02726682, demonstrated high affinity, specificity for the ATP-binding pocket of RET and drug-likeness properties. The MD simulation outputs indicated that the binding of both compounds stabilizes the RET structure and leads to fewer conformational changes.

CONCLUSIONS

The findings suggest that ZINC02092851 and ZINC02726682 may be potential inhibitors for RET, offering valuable leads for drug development against RET-associated diseases. Our study provides a promising avenue for developing new therapeutic strategies against complex diseases, including AD. Identifying natural compounds with high affinity and specificity for RET provides a valuable starting point for developing novel drugs that could help combat these debilitating diseases.

Traditional and Novel Computer-Aided Drug Design (CADD) Approaches in the Anticancer Drug Discovery Process

BACKGROUND

In the last decade, cancer has been a leading cause of death worldwide. Despite the impressive progress in cancer therapy, firsthand treatments are not selective to cancer cells and cause serious toxicity. Thus, the design and development of selective and innovative small molecule drugs is of great interest, particularly through in silico tools.

OBJECTIVE

The aim of this review is to analyze different subsections of computer-aided drug design (CADD) in the process of discovering anticancer drugs.

METHODS

Articles from the 2008-2021 timeframe were analyzed and based on the relevance of the information and the JCR of its journal of precedence, were selected to be included in this review.

RESULTS

The information collected in this study highlights the main traditional and novel CADD approaches used in anticancer drug discovery, its sub-segments, and some applied examples. Throughout this review, the potential use of CADD in drug research and discovery, particularly in the field of oncology, is evident due to the many advantages it presents.

CONCLUSION

CADD approaches play a significant role in the drug development process since they allow a better administration of resources with successful results and a promising future market and clinical wise.

Evaluation of AlphaFold2 structures as docking targets

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.

Structural Investigations on Novel Non-Nucleoside Inhibitors of Human Norovirus Polymerase

Human norovirus is the first cause of foodborne disease worldwide, leading to extensive outbreaks of acute gastroenteritis, and causing around 200,000 children to die annually in developing countries. No specific vaccines or antiviral agents are currently available, with therapeutic options limited to supportive care to prevent dehydration. The infection can become severe and lead to life-threatening complications in young children, the elderly and immunocompromised individuals, leading to a clear need for antiviral agents, to be used as treatments and as prophylactic measures in case of outbreaks. Due to the key role played by the viral RNA-dependent RNA polymerase (RdRp) in the virus life cycle, this enzyme is a promising target for antiviral drug discovery. In previous studies, following in silico investigations, we identified different small-molecule inhibitors of this enzyme. In this study, we rationally modified five identified scaffolds, to further explore structure-activity relationships, and to enhance binding to the RdRp. The newly designed compounds were synthesized according to multiple-step synthetic routes and evaluated for their inhibition of the enzyme in vitro. New inhibitors with low micromolar inhibitory activity of the RdRp were identified, which provide a promising basis for further hit-to-lead optimization.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Novel antimicrobial agents targeting the Streptococcus mutans biofilms discovery through computer technology

Dental caries is one of the most prevalent and costly biofilm-associated infectious diseases worldwide. Streptococcus mutans (S. mutans) is well recognized as the major causative factor of dental caries due to its acidogenicity, aciduricity and extracellular polymeric substances (EPSs) synthesis ability. The EPSs have been considered as a virulent factor of cariogenic biofilm, which enhance biofilms resistance to antimicrobial agents and virulence compared with planktonic bacterial cells. The traditional anti-caries therapies, such as chlorhexidine and antibiotics are characterized by side-effects and drug resistance. With the development of computer technology, several novel approaches are being used to synthesize or discover antimicrobial agents. In this mini review, we summarized the novel antimicrobial agents targeting the S. mutans biofilms discovery through computer technology. Drug repurposing of small molecules expands the original medical indications and lowers drug development costs and risks. The computer-aided drug design (CADD) has been used for identifying compounds with optimal interactions with the target via silico screening and computational methods. The synthetic antimicrobial peptides (AMPs) based on the rational design, computational design or high-throughput screening have shown increased selectivity for both single- and multi-species biofilms. These methods provide potential therapeutic agents to promote targeted control of the oral microbial biofilms in the near future.

Fibroblast growth factor 2 is a druggable target against glioblastoma: A computational investigation

Fibroblast growth factor 2 (FGF2) is a key player in cancer and tissue homeostasis and regulates renewal of several stem cell types. The FGF2 role in malignant glioma is proven and tagged FGF2, a novel druggable target, is used for developing potent drugs against glioblastoma. In this study, Asinex 51412372, Asinex 51217461, and Asinex 51216586 were filtered to show the best binding affinity for FGF2 with binding energy scores of -8.3 kcal/mol, -8.2 kcal/mol, and -7.8 kcal/mol, respectively. The compounds showed chemical interactions with several vital residues of FGF2 along the compound length. The noticeable residues that interacted with the compounds were Arg15, Asp23, Arg63, and Gln105. In dynamic investigation in solution, the FGF2 reported unstable dynamics in the first 100 ns and gained structural equilibrium in the second phase of 100 ns. The maximum root mean square deviation (RMSD) value touched by the systems is 3 Å. Similarly, the residue flexibility of FGF2 in the presence of compounds was within a stable range and is compact along the simulation time length. The compounds showed robust atomic-level stable energies with FGF2, which are dominated by both van der Waals and electrostatic interactions. The net binding energy of systems varies between -40 kcal/mol and -86 kcal/mol, suggesting the formation of strong intermolecular docked complexes. The drug-likeness and pharmacokinetic properties also pointed toward good structures that are not toxic, have high gastric absorption, showed good distribution, and readily excreted from the body. In summary, the predicted compounds in this study might be ideal hits that might be further optimized for structure and activity during experimental studies.

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors

Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.

Multi-state modeling of G-protein coupled receptors at experimental accuracy

The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 Å and 1.41 Å for the active and inactive state models, respectively. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Opportunities and Challenges for In Silico Drug Discovery at Delta Opioid Receptors

The delta opioid receptor is a Gi-protein-coupled receptor (GPCR) with a broad expression pattern both in the central nervous system and the body. The receptor has been investigated as a potential target for a multitude of significant diseases including migraine, alcohol use disorder, ischemia, and neurodegenerative diseases. Despite multiple attempts, delta opioid receptor-selective molecules have not been translated into the clinic. Yet, the therapeutic promise of the delta opioid receptor remains and thus there is a need to identify novel delta opioid receptor ligands to be optimized and selected for clinical trials. Here, we highlight recent developments involving the delta opioid receptor, the closely related mu and kappa opioid receptors, and in the broader area of the GPCR drug discovery research. We focus on the validity and utility of the available delta opioid receptor structures. We also discuss the increased ability to perform ultra-large-scale docking studies on GPCRs, the rise in high-resolution cryo-EM structures, and the increased prevalence of machine learning and artificial intelligence in drug discovery. Overall, we pose that there are multiple opportunities to enable in silico drug discovery at the delta opioid receptor to identify novel delta opioid modulators potentially with unique pharmacological properties, such as biased signaling.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

A Set of Experimentally Validated Decoys for the Human CC Chemokine Receptor 7 (CCR7) Obtained by Virtual Screening

We present a state-of-the-art virtual screening workflow aiming at the identification of novel CC chemokine receptor 7 (CCR7) antagonists. Although CCR7 is associated with a variety of human diseases, such as immunological disorders, inflammatory diseases, and cancer, this target is underexplored in drug discovery and there are no potent and selective CCR7 small molecule antagonists available today. Therefore, computer-aided ligand-based, structure-based, and joint virtual screening campaigns were performed. Hits from these virtual screenings were tested in a CCL19-induced calcium signaling assay. After careful evaluation, none of the in silico hits were confirmed to have an antagonistic effect on CCR7. Hence, we report here a valuable set of 287 inactive compounds that can be used as experimentally validated decoys.