Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

Aims: Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system.Materials & methods: This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1. A database of 2044 substances was compiled from patents.Results: For molecular similarity, the AVALON emerged as the most effective fingerprint, demonstrating an AUC-ROC of 0.963. The ANN model outperformed the Random Forest and Support Vector Classifier in cross-validation and external validation, achieving an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0 was validated through redocking and retrospective control, achieving an AUC of 0.975.Conclusions: From 15235 DrugBank compounds, 22 candidates were shortlisted. Among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising.

[Box: see text].

3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2).

Angiogenesis is an essential process in tumorigenesis, tumor invasion, and metastasis, and is an intriguing pathway for drug discovery. Targeting vascular endothelial growth factor receptor 2 (VEGFR2) to inhibit tumor angiogenic pathways has been widely explored and adopted in clinical practice. However, most drugs, such as the Food and Drug Administration -approved drug axitinib (ATC code: L01EK01), have considerable side effects and limited tolerability. Therefore, there is an urgent need for the development of novel VEGFR2 inhibitors. In this study, we propose a novel strategy to design potential candidates targeting VEGFR2 using three-dimensional (3D) deep learning and structural modeling methods. A geometric-enhanced molecular representation learning method (GEM) model employing a graph neural network (GNN) as its underlying predictive algorithm was used to predict the activity of the candidates. In the structural modeling method, flexible docking was performed to screen data with high affinity and explore the mechanism of the inhibitors. Small -molecule compounds with consistently improved properties were identified based on the intersection of the scores obtained from both methods. Candidates identified using the GEM-GNN model were selected for in silico modeling using molecular dynamics simulations to further validate their efficacy. The GEM-GNN model enabled the identification of candidate compounds with potentially more favorable properties than the existing drug, axitinib, while achieving higher efficacy.

Schistosoma excretory/secretory products: an untapped library of tolerogenic immunotherapeutics against food allergy.

Food allergy (FA) is considered the 'second wave' of the allergy epidemic in developed countries after asthma and allergic rhinitis with a steadily growing burden of 40%. The absence of early childhood pathogen stimulation embodied by the hygiene hypothesis is one explanation, and in particular, the eradication of parasitic helminths could be at play. Infections with parasites Schistosoma spp. have been found to have a negative correlation with allergic diseases. Schistosomes induce regulatory responses to evade immune detection and ensure their long-term survival. This is achieved via excretory/secretory (E/S) products, consisting of proteins, lipids, metabolites, nucleic acids and extracellular vesicles, representing an untapped therapeutic avenue for the treatment of FA without the unpleasant side-effects and risks associated with live infection. Schistosome-derived immunotherapeutic development is in its infancy and novel discoveries are heavily technology dependent; thus, it is essential to better understand how newly identified molecules interact with host immune systems to ensure safety and successful translation. This review will outline the identified Schistosoma-derived E/S products at all life cycle stages and discuss known mechanisms of action and their ability to suppress FA.

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional (3D) structure prediction of proteins. AI consists of diverse elements, including information retrieval and machine learning, and can be used in a wide range of drug discovery scenarios. In this review, we focused on AI for small-molecule drug discovery with respect to molecular design, activity prediction, and prediction of the binding poses of compounds to target molecules. We also discussed the applications of AI in academic drug discovery.

Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.

CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

Chlorogenic acid attenuates idiopathic pulmonary fibrosis: An integrated analysis of network pharmacology, molecular docking, and experimental validation.

AIMS: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung condition, the cause of which remains unknown and for which no effective therapeutic treatment is currently available. Chlorogenic acid (CGA), a natural polyphenolic compound found in different plants and foods, has emerged as a promising agent due to its anti-inflammatory, antioxidant, and antifibrotic properties. However, the molecular mechanisms underlying the therapeutic effect of CGA in IPF remain unclear. The purpose of this study was to analyze the pharmacological impact and underlying mechanisms of CGA in IPF. MAIN METHODS: Using network pharmacology analysis, genes associated with IPF and potential molecular targets of CGA were identified through specialized databases, and a protein-protein interaction (PPI) network was constructed. Molecular docking was performed to accurately select potential therapeutic targets. To investigate the effects of CGA on lung histology and key gene expression, a murine model of bleomycin-induced lung fibrosis was used. KEY FINDINGS: Network pharmacology analysis identified 384 were overlapped between CGA and IPF. Key targets including AKT1, TP53, JUN, CASP3, BCL2, MMP9, NFKB1, EGFR, HIF1A, and IL1B were identified. Pathway analysis suggested the involvement of cancer, atherosclerosis, and inflammatory processes. Molecular docking confirmed the stable binding between CGA and targets. CGA regulated the expression mRNA of EGFR, MMP9, AKT1, BCL2 and IL1B and attenuated pulmonary fibrosis in the mouse model. SIGNIFICANCE: CGA is a promising multi-target therapeutic agent for IPF, which is supported by its efficacy in reducing fibrosis through the modulation of key pathways. This evidence provides a basis to further investigate CGA as an IPF potential treatment.

Blocking the WNT/ß-catenin pathway in cancer treatment:pharmacological targets and drug therapeutic potential.

The WNT/ß-catenin signaling pathway plays crucial roles in tumorigenesis and relapse, metastasis, drug resistance, and tumor stemness maintenance. In most tumors, the WNT/ß-catenin signaling pathway is often aberrantly activated. The therapeutic usefulness of inhibition of WNT/ß-catenin signaling has been reported to improve the efficiency of different cancer treatments and this inhibition of signaling has been carried out using different methods including pharmacological agents, short interfering RNA (siRNA), and antibodies. Here, we review the WNT-inhibitory effects of some FDA-approved drugs and natural products in cancer treatment and focus on recent progress of the WNT signaling inhibitors in improving the efficiency of chemotherapy, immunotherapy, gene therapy, and physical therapy. We also classified these FDA-approved drugs and natural products according to their structure and physicochemical properties, and introduced briefly their potential mechanisms of inhibiting the WNT signaling pathway. The review provides a comprehensive understanding of inhibitors of WNT/ß-catenin pathway in various cancer therapeutics. This will benefit novel WNT inhibitor development and optimal clinical use of WNT signaling-related drugs in synergistic cancer therapy.

Protein-protein interactions in cGAS-STING pathway: a medicinal chemistry perspective.

Protein-protein interactions (PPIs) play pivotal roles in biological processes and are closely linked with human diseases. Research on small molecule inhibitors targeting PPIs provides valuable insights and guidance for novel drug development. The cGAS-STING pathway plays a crucial role in regulating human innate immunity and is implicated in various pathological conditions. Therefore, modulators of the cGAS-STING pathway have garnered extensive attention. Given that this pathway involves multiple PPIs, modulating PPIs associated with the cGAS-STING pathway has emerged as a promising strategy for modulating this pathway. In this review, we summarize an overview of recent advancements in medicinal chemistry insights into cGAS-STING PPI-based modulators and propose alternative strategies for further drug discovery based on the cGAS-STING pathway.

[Box: see text].

Complement-targeted therapeutics: Are we there yet, or just getting started?

Therapeutic interventions in the complement system, a key immune-inflammatory mediator and contributor to a broad range of clinical conditions, have long been considered important yet challenging or even unfeasible to achieve. Almost 20 years ago, a spark was lit demonstrating the clinical and commercial viability of complement-targeted therapies. Since then, the field has experienced an impressive expansion of targeted indications and available treatment modalities. Currently, a dozen distinct complement-specific therapeutics covering several intervention points are available in the clinic, benefiting patients suffering from eight disorders, not counting numerous clinical trials and off-label uses. Observing this rapid rise of complement-targeted therapy from obscurity to mainstream with amazement, one might ask whether the peak of this development has now been reached or whether the field will continue marching on to new heights. This review looks at the milestones of complement drug discovery and development achieved so far, surveys the currently approved drug entities and indications, and ventures a glimpse into the future advancements yet to come.

Recent Advances in Nanocatalyzed One-Pot Sustainable Synthesis of Bioactive N, N-Heterocycles with Anticancer Activities: An Outlook of Medicinal Chemistry.

N-heterocycles represent a predominant and unique class of organic chemistry. They have received a lot of attention due to their important chemical, biomedical, and industrial uses. Food and Drug Administration (FDA) approved about 75% of drugs containing N-based heterocycles, which are currently available in the market. N-Heterocyclic compounds exist as the backbone of numerous natural products and act as crucial intermediates for the construction of pharmaceuticals, veterinary items, and agrochemicals frequently. Among N-based heterocyclic compounds, bioactive N,N-heterocycles constitute a broad spectrum of applications in modern drug discovery and development processes. Cefozopran (antibiotic), omeprazole (antiulcer), enviradine (antiviral), liarozole (anticancer), etc., are important drugs containing N,N-heterocycles. The synthesis of N,Nheterocyclic compounds under sustainable conditions is one of the most active fields because of their significant physiological and biological properties as well as synthetic utility. Current research is demanding the development of greener, cheaper, and milder protocols for the synthesis of N,N-heterocyclic compounds to save mother nature by avoiding toxic metal catalysts, extensive application of energy, and the excessive use of hazardous materials. Nanocatalysts play a profound role in sustainable synthesis because of their larger surface area, tiny size, and minimum energy; they are eco-friendly and safe, and they provide higher yields with selectivity in comparison to conventional catalysts. It is increasingly demanding research to design and synthesize novel bioactive compounds that may help to combat cancer since the major causes of death worldwide are due to cancer. Hence, the important uses of nanocatalysts for the one-pot synthesis of biologically potent N,N-heterocycles with anticancer activities have been presented in this review.

Design, synthesis, and in vitro biological evaluation of meta-sulfonamidobenzamide-based antibacterial LpxH inhibitors.

New antibacterial compounds are urgently needed, especially for infections caused by the top-priority Gram-negative bacteria that are increasingly difficult to treat. Lipid A is a key component of the Gram-negative outer membrane and the LpxH enzyme plays an important role in its biosynthesis, making it a promising antibacterial target. Inspired by previously reported ortho-N-methyl-sulfonamidobenzamide-based LpxH inhibitors, novel benzamide substitutions were explored in this work to assess their in vitro activity. Our findings reveal that maintaining wild-type antibacterial activity necessitates removal of the N-methyl group when shifting the ortho-N-methyl-sulfonamide to the meta-position. This discovery led to the synthesis of meta-sulfonamidobenzamide analogs with potent antibacterial activity and enzyme inhibition. Moreover, we demonstrate that modifying the benzamide scaffold can alter blocking of the cardiac voltage-gated potassium ion channel hERG. Furthermore, two LpxH-bound X-ray structures show how the enzyme-ligand interactions of the meta-sulfonamidobenzamide analogs differ from those of the previously reported ortho analogs. Overall, our study has identified meta-sulfonamidobenzamide derivatives as promising LpxH inhibitors with the potential for optimization in future antibacterial hit-to-lead programs.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence.

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Expanding the inhibitor space of the WWP1 and WWP2 HECT E3 ligases.

The HECT E3 ubiquitin ligases 1 (WWP1) and 2 (WWP2) are responsible for the ubiquitin-mediated degradation of key tumour suppressor proteins and are dysregulated in various cancers and diseases. Here we expand their limited inhibitor space by identification of NSC-217913 displaying a WWP1 IC50 of 158.3 µM (95% CI = 128.7, 195.1 µM). A structure-activity relationship by synthesis approach aided by molecular docking led to compound 11 which displayed increased potency with an IC50 of 32.7 µM (95% CI = 24.6, 44.3 µM) for WWP1 and 269.2 µM (95% CI = 209.4, 347.9 µM) for WWP2. Molecular docking yielded active site-bound poses suggesting that the heterocyclic imidazo[4,5-b]pyrazine scaffold undertakes a π-stacking interaction with the phenolic group of tyrosine, and the ethyl ester enables strong ion-dipole interactions. Given the therapeutic potential of WWP1 and WWP2, we propose that compound 11 may provide a basis for future lead compound development.

A brief review on the lessons learned from COVID-19 on drug discovery and research.

COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.

CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation.

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

Metal ions in biomedically relevant macromolecular structures.

Understanding the functions of metal ions in biological systems is crucial for many aspects of research, including deciphering their roles in diseases and potential therapeutic use. Structural information about the molecular or atomic details of these interactions, generated by methods like X-ray crystallography, cryo-electron microscopy, or nucleic magnetic resonance, frequently provides details that no other method can. As with any experimental method, they have inherent limitations that sometimes lead to an erroneous interpretation. This manuscript highlights different aspects of structural data available for metal-protein complexes. We examine the quality of modeling metal ion binding sites across different structure determination methods, where different kinds of errors stem from, and how they can impact correct interpretations and conclusions.

A high content imaging assay for identification of specific inhibitors of native Plasmodium liver stage protein synthesis.

Plasmodium parasite resistance to antimalarial drugs is a serious threat to public health in malaria-endemic areas. Compounds that target core cellular processes like translation are highly desirable, as they should be capable of killing parasites in their liver and blood stage forms, regardless of molecular target or mechanism. Assays that can identify these compounds are thus needed. Recently, specific quantification of native Plasmodium berghei liver stage protein synthesis, as well as that of the hepatoma cells supporting parasite growth, was achieved via automated confocal feedback microscopy of the o-propargyl puromycin (OPP)-labeled nascent proteome, but this imaging modality is limited in throughput. Here, we developed and validated a miniaturized high content imaging (HCI) version of the OPP assay that increases throughput, before deploying this approach to screen the Pathogen Box. We identified only two hits; both of which are parasite-specific quinoline-4-carboxamides, and analogs of the clinical candidate and known inhibitor of blood and liver stage protein synthesis, DDD107498/cabamiquine. We further show that these compounds have strikingly distinct relationships between their antiplasmodial and translation inhibition efficacies. These results demonstrate the utility and reliability of the P. berghei liver stage OPP HCI assay for the specific, single-well quantification of Plasmodium and human protein synthesis in the native cellular context, allowing the identification of selective Plasmodium translation inhibitors with the highest potential for multistage activity.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.