Protein click chemistry and its potential for medical applications.

A revolution in chemical biology occurred with the introduction of click chemistry. Click chemistry plays an important role in protein chemistry modifications, providing specific, sensitive, rapid, and easy-to-handle methods. Under physiological conditions, click chemistry often overlaps with bioorthogonal chemistry, defined as reactions that occur rapidly and selectively without interfering with biological processes. Click chemistry is used for the posttranslational modification of proteins based on covalent bond formations. With the contribution of click reactions, selective modification of proteins would be developed, representing an alternative to other technologies in preparing new proteins or enzymes for studying specific protein functions in different biological processes. Click-modified proteins have potential in diverse applications such as imaging, labeling, sensing, drug design, and enzyme technology. Due to the promising role of proteins in disease diagnosis and therapy, this review aims to highlight the growing applications of click strategies in protein chemistry over the last two decades, with a special emphasis on medicinal applications.

Integrated Computational Approaches for Drug Design Targeting Cruzipain.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions.

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

De novo design of drug-binding proteins with predictable binding energy and specificity.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery.

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Design, synthesis, insilco study and biological evaluation of new isatin-sulfonamide derivatives by using mono amide linker as possible as histone deacetylase inhibitors.

OBJECTIVE: Aim: To evaluate the cytotoxic activity of newly synthesized a series of novel HDAC inhibitors comprising sulfonamide as zinc binding group and Isatin derivatives as cap group joined by mono amide linker as required to act as HDAC inhibitors. PATIENTS AND METHODS: Materials and Methods: The utilization of sulfonamide as zinc binding group joined by N-alkylation reaction with ethyl-bromo hexanoate as linker group that joined by amide reaction with Isatin derivatives as cap groups which known to possess antitumor activity in the designed of new histone deacetylase inhibitors and using the docking and MTT assay to evaluate the compounds. RESULTS: Results: Four compounds have been synthesized and characterized successfully by ART-FTIR, NMR and ESI-Ms. the compounds were synthesized and characterized by successfully by ART-FTIR, NMR and ESI- Ms. Assessed for their cytotoxic activity against human colon adenocarcinoma MCF-7 (IC50, I=105.15, II=60.00, III=54.11, IV=56.57, vorinostat=28.41) and hepatoblastoma HepG2 (IC50, I=63.91, II=135.18, III=118.85, IV=51.46, vorinostat=37.50). Most of them exhibited potent HDAC inhibitory activity and significant cytotoxicity. CONCLUSION: Conclusions: The synthesized compounds (I, II, III and IV) showed cytotoxicity toward MCF-7 and HepG2 cancer cell lines and their docking analysis provided a preliminary indication that they are viable [HDAC6] candidates.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Synthesis, docking study, and antitumor evaluation of benzamides and oxadiazole derivatives of 3-phenoxybenzoic acid as VEGFR-2 inhibitors.

Current chemotherapeutic agents have several limitations, including lack of selectivity, the development of undesirable side effects, and chemoresistance. As a result, there is an unmet need for the development of novel small molecules with minimal side effects and the ability to specifically target tumor cells. A new series of 3-phenoxybenzoic acid derivatives, including 1,3,4-oxadiazole derivatives (4a-d) and benzamides derivatives (5a-e) were synthesized; their chemical structures were confirmed by Fourier-transform infrared spectroscopy, 1H nuclear magnetic resonance (NMR), 13C NMR, and mass spectra; and various physicochemical properties were determined. The antiproliferative activities of the new derivatives were evaluated by means of the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Three compounds (4b, 4c, and 4d) exhibited cytotoxicity against two of the three cell lines tested, five compounds (3, 4a, 5a, 5b, and 5e) were toxic to one cell line, while two compounds (5c and 5d) were not cytotoxic to any of the three cell lines tested in the current study. Based on docking scores, MTT assay findings, and vascular endothelial growth factor receptor 2 (VEGFR-2) kinase activity data, Compound 4d was selected for further biological investigation. Flow cytometry was used to determine the mode of cell death (apoptosis vs. necrosis) and the effect on cell cycle progression. Compound 4d arrested HepG2 hepatocellular carcinoma cells in the G2/M phase and activated both the intrinsic and extrinsic apoptosis pathways. In conclusion, Compound 4d has shown promising results for future research as a potent VEGFR-2 tyrosine kinase inhibitor.

High-resolution cryo-EM of the human CDK-activating kinase for structure-based drug design.

Rational design of next-generation therapeutics can be facilitated by high-resolution structures of drug targets bound to small-molecule inhibitors. However, application of structure-based methods to macromolecules refractory to crystallization has been hampered by the often-limiting resolution and throughput of cryogenic electron microscopy (cryo-EM). Here, we use high-resolution cryo-EM to determine structures of the CDK-activating kinase, a master regulator of cell growth and division, in its free and nucleotide-bound states and in complex with 15 inhibitors at up to 1.8 Å resolution. Our structures provide detailed insight into inhibitor interactions and networks of water molecules in the active site of cyclin-dependent kinase 7 and provide insights into the mechanisms contributing to inhibitor selectivity, thereby providing the basis for rational design of next-generation therapeutics. These results establish a methodological framework for the use of high-resolution cryo-EM in structure-based drug design.

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

Designing Potent Anti-Cancer Agents: Synthesis and Molecular Docking Studies of Thieno[2,3-d][1,2,4]triazolo[1,5-a]pyrimidine Derivatives.

A new series of thieno[2,3-d][1,2,4]triazolo[1,5-a]pyrimidines was designed and synthesized using readily available starting materials, specifically, ß-enaminoester. Their cytotoxicity was screened against three cancer cell lines, namely, MCF-7, HCT-116, and PC-3. 2-(4-bromophenyl)triazole 10b and 2-(anthracen-9-yl)triazole 10e afforded excellent potency against MCF-7 cell lines (IC50 = 19.4 ± 0.22 and 14.5 ± 0.30 µM, respectively) compared with doxorubicin (IC50 = 40.0 ± 3.9 µM). The latter derivatives 10b and 10e were further subjected to in silico ADME and docking simulation studies against EGFR and PI3K and could serve as ideal leads for additional modification in the field of anticancer research.

Design, Synthesis, and Biological Evaluation of Novel Phenoxy Acetic Acid Derivatives as Selective COX-2 Inhibitors Coupled with Comprehensive Bio-Pharmacological Inquiry, Histopathological Profiling, and Toxicological Scrutiny.

COX-2 plays a key role in converting arachidonic acid into prostaglandins. This makes it a significant target for treating inflammation. Selective COX-2 inhibitors have marked a new phase in inflammatory treatment, providing significant effectiveness while reducing negative side effects. Herein, we aimed at the design and synthesis of new anti-inflammatory agents 5a-f, 7a-b, 10a-f, and 13a-b with expected selective inhibition for COX-2. Compounds 5d-f, 7b, and 10c-f showed significant COX-2 inhibition with IC50 in the range of 0.06-0.09 µM, indicating powerful pharmacological potential. In light of this, eight compounds were selected for further testing in vivo to assess their selectivity toward COX-1/COX-2 enzymes with the ability to reduce paw thickness. Compounds 5f and 7b showed significant anti-inflammatory effects without causing stomach ulcers, as they showed significant in vivo inhibition for paw thickness at 63.35% and 46.51%, as well as paw weight at 68.26% and 64.84%. Additionally, the tested compounds lowered TNF-α by 61.04% and 64.88%, as well as PGE-2 by 60.58% and 57.07%, respectively. Furthermore, these potent compounds were thoroughly analyzed for their pain-relieving effects, histological changes, and toxicological properties. Assessing renal and stomach function, as well as measuring liver enzymes AST and ALT, together with kidney indicators creatinine and urea, offered valuable information on their safety profiles. Molecular modeling studies explain the complex ways in which the strong interacts with the COX-2 enzyme. This comprehensive strategy emphasizes the therapeutic potential and safety profiling of these new analogues for managing inflammation.

Recent developments of HSP90 inhibitors: an updated patent review (2020-present).

INTRODUCTION: The 90-kDa heat shock protein (HSP90) functions as a molecular chaperone, it assumes a significant role in diseases such as cancer, inflammation, neurodegeneration, and infection. Therefore, the research and development of HSP90 inhibitors have garnered considerable attention. AREAS COVERED: The primary references source for this review is patents obtained from SciFinder, encompassing patents on HSP90 inhibitors from the period of 2020 to 2023.This review includes a thorough analysis of their structural attributes, pharmacological properties, and potential clinical utilities. EXPERT OPINION: In the past few years, HSP90 inhibitors targeting ATP binding pocket are still predominate and one of them has been launched, besides, novel drug design strategies like C-terminal targeting, isoform selective inhibiting and bifunctional molecules are booming, aiming to improve the efficacy and safety. With expanded drug types and applications, HSP90 inhibitors may gradually becoming a sagacious option for treating various diseases.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Recent Advances in Automated Structure-Based De Novo Drug Design.

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.

C3-Symmetric ligands in drug design: An overview of the challenges and opportunities ahead.

C3-symmetry is a type of star-shaped molecule consisting of a central core and three symmetrically attached chains. These molecules are used in drug discovery due to their unique three-fold rotational symmetry, which allows for specific binding interactions and improved molecular recognition. In this text, we provide an overview of synthetic approaches with C3-symmetry as a pharmaceutical tool: progress, challenges, and opportunities. C3-symmetric ligands offer both challenges and opportunities in drug design. Their unique symmetry can enhance binding interactions, but careful consideration of rigidity, synthetic complexity, and target compatibility is crucial. Further research and advancements in synthetic methods and modeling tools will likely drive their exploration in drug discovery, leading to the discovery of potent C3-symmetric ligands.

Identification of Novel Bisamide-Decorated Benzotriazole Derivatives as Anti-Phytopathogenic Virus Agents: Bioactivity Evaluation and Computational Simulation.

As a notorious phytopathogenic virus, the tobacco mosaic virus (TMV) severely reduced the quality of crops worldwide and caused critical constraints on agricultural production. The development of novel virucides is a persuasive strategy to address this predicament. Herein, a series of novel bisamide-decorated benzotriazole derivatives were elaborately prepared and screened. Biological tests implied that the optimized compound 7d possessed the most brilliant antiviral inactive profile (EC50 = 157.6 µg/mL) and apparently surpassed that of commercial ribavirin (EC50 = 442.1 µg/mL) 2.8-fold. The preliminary antiviral mechanism was elaborately investigated via transmission electron microscopy, microscale thermophoresis (MST) determination, RT-qPCR, and Western blot analysis. The results showed that compound 7d blocked the assembly of TMV by binding with coat protein (Kd = 0.7 µM) and suppressed TMV coat protein gene expression and biosynthesis process. Computational simulations indicated that 7d displayed strong H-bonds and pi interactions with TMV coat protein, affording a lower binding energy (ΔGbind = -17.8 kcal/mol) compared with Ribavirin (ΔGbind = -10.7 kcal/mol). Overall, current results present a valuable perception of bisamide decorated benzotriazole derivatives with appreciably virustatic competence and should be profoundly developed as virucidal candidates in agrochemical.

Discovery of Novel Chiral Indole Derivatives Containing the Oxazoline Moiety as Potential Antiviral Agents for Plants.

Potato virus Y (PVY) is an important plant virus that has spread worldwide, causing significant economic losses. To search for novel structures as potent antiviral agents, a series of chiral indole derivatives containing oxazoline moieties were designed and synthesized and their anti-PVY activities were evaluated. Biological activity tests demonstrated that many chiral compounds exhibited promising anti-PVY activities and that their absolute configurations exhibited obvious distinctions in antiviral bioactivities. Notably, compound (S)-4v displayed excellent curative and protective efficacy against PVY, with EC50 values of 328.6 and 256.1 µg/mL, respectively, which were superior to those of commercial virucide ningnanmycin (NNM, 437.4 and 397.4 µg/mL, respectively). The preliminary antiviral mechanism was investigated to determine the difference in antiviral activity between the two enantiomers of 4v chiral compounds. Molecular docking indicated a stronger binding affinity between the coating proteins of PVY (PVY-CP) and (S)-4v (-6.5 kcal/mol) compared to (R)-4v (-6.2 kcal/mol). Additionally, compound (S)-4v can increase the chlorophyll content and defense-related enzyme activities more effectively than its enantiomer. Therefore, this study provides an important basis for the development of chiral indole derivatives containing oxazoline moieties as novel agricultural chemicals.

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform.

Cyclin-dependent kinases (CDKs) are critical cell cycle regulators that are often overexpressed in tumors, making them promising targets for anti-cancer therapies. Despite substantial advancements in optimizing the selectivity and drug-like properties of CDK inhibitors, safety of multi-target inhibitors remains a significant challenge. Macrocyclization is a promising drug discovery strategy to improve the pharmacological properties of existing compounds. Here we report the development of a macrocyclization platform that enabled the highly efficient discovery of a novel, macrocyclic CDK2/4/6 inhibitor from an acyclic precursor (NUV422). Using dihedral angle scan and structure-based, computer-aided drug design to select an optimal ring-closing site and linker length for the macrocycle, we identified compound 8 as a potent new CDK2/4/6 inhibitor with optimized cellular potency and safety profile compared to NUV422. Our platform leverages both experimentally-solved as well as generative chemistry-derived macrocyclic structures and can be deployed to streamline the design of macrocyclic new drugs from acyclic starting compounds, yielding macrocyclic compounds with enhanced potency and improved drug-like properties.