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Motivation: Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could Show less

Abstract ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL’s content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements. Show less

Background The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug–drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. Methods This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified Show less

Title: Mitochondrial Viscosity Probes: Iridium(III) Complexes Induce Apoptosis in HeLa Cells. Abstract: Mitochondrial viscosity has emerged as a promising biomarker for diseases such as cancer and neurodegenerative disorders, yet accurately measuring viscosity at the subcellular level remains a significant challenge. In this study, we synthesized and characterized three cyclometalated iridium(III) complexes (Ir1-Ir3) containing 5-fluorouracil derivatives as ligands. Among these, Ir1 selectively induced apoptosis in HeLa cells by increasing mitochondrial production of reactive oxygen species (ROS), which triggered a cascade of events leading to mitochondrial dysfunction. Additionally, the fluorescence lifetime of Ir1 demonstrated high sensitivity to intracellular viscosity changes, enabling real-time fluorescence lifetime imaging microscopy (FLIM) of cellular micro-viscosity during apoptosis. These findings underscore the potential of cyclometalated Ir(III) complexes for both therapeutic and diagnostic applications at the subcellular level. Show less

Abstract The review is devoted to copper coordination compounds based on 2,2′-bipyridine / 1,10-phenanthroline and diverse N-, O-, S-donor ligands exhibiting cytotoxic properties. Therefore, they can be a starting platform for developing antitumor drugs. The review covers the structural aspects of the complexes, the features of their cytotoxic activity and its mechanism, as well as in vivo studies. Show less

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located Show less

It is well established that oxaliplatin, one of the three Pt(II) anticancer drugs approved worldwide, and phenanthriplatin, an important preclinical monofunctional Pt(II) anticancer drug, possess a different mode of action from that of cisplatin and carboplatin, namely, the induction of nucleolar stress. The exact mechanisms that lead to Pt-induced nucleolar stress are, however, still poorly understood. As such, studies aimed at better understanding the biological targets of both oxaliplatin and phenanthriplatin are urgently needed to expand our understanding of Pt-induced nucleolar stress and guide the future design of Pt chemotherapeutics. One approach that has seen great success in the past is the use of Pt-click complexes to study the biological targets of Pt drugs. Herein, we report the synthesis and characterization of the first examples of click-capable phenanthriplatin complexes. Furthermore, through monitoring the relocalization of nucleolar proteins, RNA transcription levels, and DNA damage repair biomarker γH2AX, and by investigating their in vitro cytotoxicity, we show that these complexes successfully mimic the cellular responses observed for phenanthriplatin treatment in the same experiments. The click-capable phenanthriplatin derivatives described here expand the existing library of Pt-click complexes. Significantly they are suitable for studying nucleolar stress mechanisms and further elucidating the biological targets of Pt complexes. Show less

Metal-based drugs hold promise as potent anticancer agents owing to their unique interactions with cellular targets. This review discusses recent advances in our understanding of the intricate molecular interactions of metal-based anticancer compounds with specific therapeutic targets in cancer cells. Advanced computational and experimental methodologies delineate the binding mechanisms, structural dynamics and functional outcomes of these interactions. In addition, the review sheds light on the precise modes of action of these drugs, their efficacy and the potential avenues for further optimization in cancer-treatment strategies and the development of targeted and effective metal-based therapies for combating various forms of cancer. Show less

In the era of high throughput sequencing, special software is required for the clinical evaluation of genetic variants. We developed REEV (Review, Evaluate and Explain Variants), a user-friendly platform for clinicians and researchers in the field of rare disease genetics. Supporting data was aggregated from public data sources. We compared REEV with seven other tools for clinical variant evaluation. REEV (semi-)automatically fills individual ACMG criteria facilitating variant interpretation. REEV can store disease and phenotype data related to a case to use these for phenotype similarity measures. Users can create public permanent links for individual variants that can be saved as browser bookmarks and shared. REEV may help in the fast diagnostic assessment of genetic variants in a clinical as well as in a research context. REEV (https://reev.bihealth.org/) is free and open to all users and there is no login requirement. Show less

Gold(i) terpyridine derivatives exhibiting exceptional cytotoxic activity against critical cancer types are elucidated. Significantly, these compounds demonstrate dual functionality by interacting with DNA and inhibiting TrxR. Show less

Indexing articles by their publication type and study design is essential for efficient search and filtering of the biomedical literature, but is understudied compared to indexing by MeSH topical terms. In this study, we leveraged the human-curated publication types and study designs in PubMed to generate a dataset of more than 1.2M articles (titles and abstracts) and used state-of-the-art Transformer-based models for automatic tagging of publication types and study designs. Specifically, we trained PubMedBERT-based models using a multi-label classification approach, and explored undersampling, feature verbalization, and contrastive learning to improve model performance. Our results show that PubMedBERT provides a strong baseline for publication type and study design indexing; undersampling, feature verbalization, and unsupervised constrastive loss have a positive impact on performance, whereas supervised contrastive learning degrades the performance. We obtained the best overall performance with 80% undersampling and feature verbalization (0.632 macro-F1, 0.969 macro-AUC). The model outperformed previous models (MultiTagger) across all metrics and the performance difference was statistically significant (p < 0.001). Despite its stronger performance, the model still has room for improvement and future work could explore features based on full-text as well as model interpretability. We make our data and code available at https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/AMIA. Show less

Small molecule molecular glues can nucleate protein complexes and rewire interactomes. Molecular glues are widely used as probes for understanding functional proximity at a systems level, and the potential to instigate event-driven pharmacology has motivated their application as therapeutics. Despite advantages such as cell permeability and the potential for low off-target activity, glues are still rare when compared to canonical inhibitors in therapeutic development. Their often simple structure and specific ability to reshape protein-protein interactions pose several challenges for widespread, designer applications. Molecular glue discovery and design campaigns can find inspiration from the fields of synthetic biology and biophysics to mine chemical libraries for glue-like molecules. Show less

Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions. Show less

AbstractG-quadruplex DNA secondary structures are formed in guanine-rich sequences and have been found to play an important role in regulating different biological processes. Indeed, guanine-rich sequences with the potential to form G-quadruplexes are present in different regions in the human genome, such as telomeres and the promoter region of different genes, including oncogene promoters. Thus, the rational design of small molecules capable of interacting, stabilising or damaging with high specificity these secondary structures represents an important strategy for the development of potent anticancer drugs. In this review, we highlight the interaction between G-quadruplex structures and their ligands, specifically emphasising the role of metal complexes. We provide detailed structural insight into the binding modes of metal complex-G-quadruplex interaction by analysing 18 sets of coordinates from X-ray and NMR currently available in the Protein Data Bank (PDB), with a primary focus on X-ray structural data. Show less

Background Hypoxia is a common feature of many solid tumors and causes radiotherapy and immunotherapy resistance. Pharmacological inhibition of oxidative phosphorylation (OXPHOS) has emerged as a therapeutic strategy to reduce hypoxia. However, the OXPHOS inhibitors tested in clinical trials caused only moderate responses in hypoxia alleviation or trials were terminated due to dose-limiting toxicities. To improve the therapeutic benefit, FDA approved OXPHOS inhibitors (e.g. atovaquone) were conjugated to triphenylphosphonium ­( TPP+) to preferentially target Show less

Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered to be undruggable targets have become increasingly feasible due to the progress that has been made over the last two decades and the rapid technological advances. This work explores the influence of technological innovations on PPI research and development. Additionally, the diverse strategies for discovering, modulating, and characterizing PPIs and their corresponding modulators are examined with the aim of presenting a streamlined pipeline for advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in PPI modulator discovery and development, we aim to illustrate the efficacy of various strategies for identifying, optimizing, and overcoming challenges associated with PPI modulator design. The valuable lessons and insights gained from the identification, optimization, and approval of PPI modulators are discussed with the aim of demonstrating that PPI modulators have transitioned beyond early-stage drug discovery and now represent a prime opportunity with significant potential. The selected examples of PPI modulators encompass those developed for cancer, inflammation and immunomodulation, as well as antiviral applications. This perspective aims to establish a foundation for the effective targeting and modulation of PPIs using PPI modulators and pave the way for future drug development. Show less

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress and dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known cell death mechanisms. This process involves the interaction of copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation and cell death. Recently, a growing number of studies have explored the link between cuproptosis and cancer development. This review comprehensively examines the systemic and cellular metabolism of copper, including tumor-related signaling pathways influenced by copper. It delves into the discovery and mechanisms of cuproptosis and its connection to various cancers. Additionally, the review suggests potential cancer treatments using copper ionophores that induce cuproptosis, in combination with small molecule drugs, for precision therapy in specific cancer types. Show less

Over the past three decades, high-throughput phenotypic cancer cell line screens have revealed unanticipated small-molecule activities and illuminated connections between tumor genotypes and anticancer efficacy. Founded in 1984, the National Cancer Institute's "NCI60" screen laid the conceptual groundwork for the contemporary landscape of phenotypic drug discovery. NCI60 first operated as a primary bioactivity screen, but molecular characterization of the NCI60 cell line panel and development of a small-molecule sensitivity pattern recognition algorithm (called "COMPARE") have enabled subsequent studies into drug mechanisms of action and biomarker identification. In this issue of Cancer Research, Kunkel and colleagues report an updated version of the NCI60 screen, dubbed "HTS384" NCI60, that better aligns with current cell proliferation assay standards and has higher throughput. Changes include the use of a 384-well plate format, automated laboratory equipment, 3 days of compound exposure, and a CellTiter-Glo luminescent endpoint. To confirm that data from the HTS384 and classic NCI60 screen are comparable, the authors tested a library of 1,003 anticancer agents using both protocols and applied COMPARE to analyze patterns of cell line sensitivities. More than three dozen groups of targeted therapies showed high comparability between screens. Modernization of NCI60, and closer integration with other large-scale pharmacogenomic screens and molecular feature sets, will help this public screening service remain pertinent for cancer drug discovery efforts for years to come. See related article by Kunkel et al., p. 2403. Show less

Although 5-fluorouracil (5-FU) is the primary chemotherapy treatment for colorectal cancer (CRC), its efficacy is limited by drug resistance. Ferroptosis activation is a promising treatment for 5-FU-resistant cancer cells; however, potential therapeutic targets remain elusive. This study investigated ferroptosis vulnerability and dihydroorotate dehydrogenase (DHODH) activity using stable, 5-FU-resistant CRC cell lines and xenograft models. Ferroptosis was characterized by measuring malondialdehyde levels, assessing lipid metabolism and peroxidation, and using mitochondrial imaging and assays. DHODH function is investigated through gene knockdown experiments, tumor behavior assays, mitochondrial import reactions, intramitochondrial localization, enzymatic activity analyses, and metabolomics assessments. Intracellular lipid accumulation and mitochondrial DHODH deficiency led to lipid peroxidation overload, weakening the defense system of 5-FU-resistant CRC cells against ferroptosis. DHODH, primarily located within the inner mitochondrial membrane, played a crucial role in driving intracellular pyrimidine biosynthesis and was redistributed to the cytosol in 5-FU-resistant CRC cells. Cytosolic DHODH, like its mitochondrial counterpart, exhibited dihydroorotate catalytic activity and participated in pyrimidine biosynthesis. This amplified intracellular pyrimidine pools, thereby impeding the efficacy of 5-FU treatment through molecular competition. These findings contribute to the understanding of 5-FU resistance mechanisms and suggest that ferroptosis and DHODH are promising therapeutic targets for patients with CRC exhibiting resistance to 5-FU. Show less

AbstractArtificial metallo‐nucleases (AMNs) are small molecule DNA cleavage agents, also known as DNA molecular scissors, and represent an important class of chemotherapeutic with high clinical potential. This review provides a primary level of exploration on the concepts key to this area including an introduction to DNA structure, function, recognition, along with damage and repair mechanisms. Building on this foundation, we describe hybrid molecules where AMNs are covalently attached to directing groups that provide molecular scissors with enhanced or sequence specific DNA damaging capabilities. As this research field continues to evolve, understanding the applications of AMNs along with synthetic conjugation strategies can provide the basis for future innovations, particularly for designing new artificial gene editing systems. Show less

In a recent series of papers, James W. Lee reported that mitochondrial oxidative phosphorylation violates the second law of thermodynamics and that it is allowed to do so because it is a "Type-B" process that features lateral and longitudinal membrane asymmetry. We show here that these contentions are based on problematic interpretations of the literature. More reliable values of ΔG_redox and ΔG_{ATP synthesis} show that the second law is not violated. More recent reports on the structures of the redox-driven proton pumps (Complexes I, III, and IV) suggest that longitudinal membrane asymmetry does not exist. Finally, Lee's predictions for the concentration of protons localized at the P-side surface of the bioenergetic membrane are likely to be much too high due to several errors; thus, his predicted high values of ΔpH_surface that violate the second law are likely to be wrong. There is currently no strong experimental or theoretical evidence to support the contention that oxidative phosphorylation violates the second law of thermodynamics. Show less

Background: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. ARTICLE HISTORY Objective: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. Received: June 07, 2024 Revised: August 11, 2024 Accepted: August 19, 2024 Methods: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literaDOI: 10.2174/0113894501330963240905083020 ture. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. Results: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. Conclusion: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process. Show less

Lung cancer is a leading cause of cancer-related death worldwide that needs updated therapies to contrast both the serious side effects and the occurrence of drug resistance. A panel of non-small cell lung cancer (NSCLC) cells were herein employed as cancer models. Eight structurally related gold(I) and gold(III) complexes with NHC and halides or triphenylphosphane ligands were investigated as lung cancer cell growth inhibitors. As expected, gold compounds with PPh3 were found to be more cytotoxic than homoleptic [(NHC)2-Au(I)]X or heteroleptic NHC-Au(I)X or NHC-Au(III)X3 complexes. Mixed ligand gold(I) compounds exhibiting the linear NHC-AuPPh3 (compound 7) or the trigonal NHC-Au(Cl)PPh3 (compound 8) arrangements at the central metal were found to be the best lung cancer cytotoxic compounds. Analysis of the TrxR residual activity of the treated cells revealed that these compounds efficiently inhibit the most accredited molecular target for gold compounds, the TrxR, with compound 8 reaching more than 80% activity reduction in lung cells. Some of the current cancer lung therapy protocols consist of specific lung cancer cell cytotoxic agents combined with antifolate drugs; interestingly, the herein gold compounds are both TrxR and antifolate inhibitors. The human DHFR was inhibited with IC50 ranging between 10–21 µM, depending on substrate concentrations, proceeding by a likely allosteric mechanism only for compound 8. Show less

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements. Show less