GPR18, a class A G protein-coupled receptor once considered an orphan with unclear links to the endocannabinoid system (ECS), is increasingly recognized as a pharmacologically relevant component of the extended ECS. Structural, pharmacological, and functional evidence increasingly support the positioning of GPR18 within the extended ECS, with closer alignment to cannabinoid receptor 2 (CB2R) than to cannabinoid receptor 1 (CB1R) at the levels of signaling, tissue distribution, and lipid-mediated pharmacology. This review provides an updated overview of GPR18 biology through distinctive analytical approaches. First, a systematic comparison between AlphaFold-predicted structures and previously published homology models refines current understanding of conserved motifs, constitutive activity determinants, and the dynamic architecture of the ligand-binding pocket. Second, literature mining combined with open-access transcriptomic and proteomic data from the Human Protein Atlas delineates GPR18 expression across human tissues and organs, revealing preferential expression in immune, vascular, and neuroglial compartments. Third, the emerging role of GPR18 in neuroinflammation is examined, exploring its potential involvement in resolution pathways, microglial responses, and neuroprotective mechanisms, in some cases through a proposed functional interplay with CB2R. Finally, in silico chemoinformatic approaches, including PLATO-based target fishing, are employed to map the broader network engaged by the main GPR18 ligands, and to explore strategies toward the design of dual GPR18/CB2R modulators. Collectively, these evidences contribute to repositioning GPR18 from an elusive orphan G protein-coupled receptor to a characterized component of the (endo)cannabinoid interactome, with emerging promise as a potential therapeutic target for neuroinflammatory and neurodegenerative disorders, pending further pharmacological and clinical validation.

Epidermal Growth Factor Receptor (EGFR) signalling plays a key role in the progression of chronic kidney disease (CKD), as its persistent activation promotes inflammation, fibrosis, and tubular cell proliferation. Aberrant EGFR expression and overactivation have therefore been associated with renal injury and disease progression, making it a relevant biomarker and potential therapeutic target in CKD. Our goal was to repurpose known fluorescent ligands, originally developed for other targets, to study EGFR expression at the kidney cell membrane in healthy and diseased conditions, providing a potential tool for disease detection and staging. Drug repurposing leverages existing pharmacokinetic and safety data, reducing time and costs, and can transform off-target effects into new applications through structure-based and AI-driven approaches. We built a database of 47 commercially available fluorescent ligands with fully disclosed structures and screened them by molecular docking against EGFR. Two promising candidates were identified and validated through preliminary kinase inhibition assays. Their performance was then evaluated in three cell lines with different EGFR expression levels (RCC, HK-2, and EGFR-negative EA.hy926). Notably, the harmonic integration of in silico and experimental approaches proved effective in the rational repurposing of two fluorescent organic ligands as probes for the detection of EGFR overexpression in CKD.

Monastrol, a DHPM-based Eg5 inhibitor with well-known antiproliferative activity but limited therapeutic potential due to poor solubility and low bioavailability, was selected as the lead compound for the design of styryl-modified 3,4-dihydropyrimidin-2(1H)-ones with an improved pharmaceutical profile. Twelve derivatives (10-21) were synthesized via the Biginelli reaction and evaluated for cytotoxicity in HeLa and MCF-7 cells. Styryl derivatives 16 and 17 emerged as the most active. In HeLa cells, derivatives 17 (IC50 = 1.3 µM) and 16 (IC50 = 3.7 µM) were approximately 85-fold and 30-fold more potent than monastrol (IC50 = 111 µM), respectively. In MCF-7 cells, derivatives 16 and 17 displayed 18- to 20-fold higher potency than monastrol, respectively. Biological results also indicate that styryl derivatives 16 and 17 induce apoptosis in both HeLa and MCF-7 cells. In HeLa cells, activation of caspase-8, -9, and -3 suggests the involvement of both intrinsic and extrinsic pathways. In contrast, in MCF-7 cells, the increased expression of p53 and p21, together with PARP cleavage, suggests a p53-dependent apoptotic response. Derivatives 16 and 17 emerged as promising Eg5 inhibitors from docking studies, but their poor aqueous solubility (0.2-0.7 µM), despite high biological stability, highlights the need for formulation strategies to improve drug-like properties.

A classical one-drug-one-target approach is ineffective against diseases with a multi-factorial pathogenesis, such as Alzheimer's disease (AD). On the other hand, multitarget approaches can provide a higher level of pharmacological interference which can better affect the disease network. Acetylcholinesterase (AChE), beta-site amyloid precursor protein cleaving enzyme 1 (β-secretase, BACE-1), glycogen synthase kinase 3 beta (GSK-3β), monoamine oxidases (MAOs), metal ions in the brain, N-methyl-D-aspartate (NMDA) receptor, 5-hydroxytryptamine (5-HT) receptors, the third subtype of histamine receptor (H3 receptor), and phosphodiesterases (PDEs) are the main major targets of this network whose connection are still far from being fully understood. Aware of this limitation, we herein focus on the main chemotypes employed for AChE/BACE-1 targeting. These include mostly bioactive compounds based on chalcones, triazines, triazoles, piperidines, and flavonoids.

Endocrine disruption remains a major concern in predictive toxicology, demanding accurate and interpretable models to assess molecular interactions with hormonal pathways. Here, we present a descriptor-free bidirectional long short-term memory (BiLSTM) framework designed to predict the toxicity of chemicals toward androgen (AR) and estrogen receptors (ER), two of the most complex and biologically relevant end points in toxicology. The model operates directly on SMILES strings, which are tokenized and converted into one-hot encoded sequences, enabling the automatic extraction of chemically meaningful representations without reliance on handcrafted molecular descriptors or fingerprints. To enhance interpretability, we introduce a novel explainable artificial intelligence (XAI) approach that aggregates character-level attribution scores into color-coded substructures, revealing features that drive or reduce toxicity and offering mechanistic insight into receptor-mediated effects. The models were trained on publicly available, high-quality data sets comprising 1664 and 1529 chemicals with experimental binary labels for AR and ER, respectively. Employing cross-validation analyses, based on 20% randomly stratified resampling iterated 10 times, the proposed workflow returned accuracy equal to 0.75 ± 0.08 and 0.81 ± 0.05, sensitivity equal to 0.66 ± 0.36 and 0.69 ± 0.17, and specificity equal to 0.76 ± 0.14 and 0.82 ± 0.06 for AR and ER end points, respectively. Our descriptor-free models ensure highly transparent results with a substructure level interpretability. These findings demonstrate the potential of deep learning directly on molecular textual representations to advance predictive toxicology and to support mechanistic understanding in chemical risk assessment.

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder involving a complex interplay of interconnected pharmacological targets, a feature that limits the success of traditional single-target medicinal chemistry approaches. Here, we present MINERVA (Multi-target Interactive Network for Explainable Research and Visualization in Alzheimer’s disease), a public web-based platform designed to support multi-target drug discovery assisted by eXplainable Artificial Intelligence (XAI) strategies. MINERVA integrates large-scale disease-focused high-quality data comprising as many as 70,960 small molecules annotated across 33 AD relevant targets, taken from ChEMBL and CADRO databases. To capture different levels of pharmacological relevance, four distinct pharmacological thresholds (i.e., 10 μM, 1 μM, 100 nM, and 10 nM) were set to enable the parallel exploration of weak to high-affinity ligand spaces. Independent Balanced Random Forest (BRF) classifiers were trained for each AD target threshold combination using an extended core-substituent fingerprint, which ensures robustness against class imbalance and chemical heterogeneity. MINERVA incorporates a probability binning based domain of applicability (DoA) to quantify prediction reliability and a SHAP-based explainability framework to fairly map fragment-level contributions directly onto chemical structures. MINERVA is freely accessible at https://prometheus.farmacia.uniba.it/minerva/. Scientific contribution Herein we introduce MINERVA, the first freely accessible platform that combines large-scale AD-related data curation, multi-target prediction, and multi-threshold bioactivity modeling within a fully explainable and user-friendly environment. By enabling transparent, ligand-based exploration of chemical space across multiple AD pathways, MINERVA provides a unique and practical resource for accelerating multi-target drug discovery in the neurodegenerative research area.

The withdrawal of numerous approved drugs in late development stages, or even from the market, due to safety concerns remains a major challenge, contributing to the high attrition rate in drug discovery and development. Among these concerns, cardiotoxicity is a critical toxicological issue, particularly in oncology, as drugs can induce heart damage by triggering pathological conditions such as arrhythmia, myocardial infarction, and myocardial hypertrophy. Here, we introduce CUPID (Cardiotox Understanding Platform for Intelligent Drug Discovery), an explainable artificial intelligence (XAI) framework designed to predict cardiotoxicity associated with ERG (ether-à-go-go-related gene) potassium, Nav1.5 sodium, and Cav1.2 calcium ion channels. The framework was trained using three carefully curated interspecies experimental datasets from the latest ChEMBL database (release 34) and the CSFP (Core-Substituent Fingerprint), which encodes molecular fragments derived from the decomposition of drug-like small molecules. By leveraging these experimental datasets, highly accurate explainable machine learning models were developed, achieving approximately 80 % accuracy in 5-fold stratified cross-validation analyses. CUPID provides a comprehensive risk assessment of early cardiotoxicity and a key feature is its interpretability: predictions are annotated with clear applicability domain information, while chemical substructures linked to cardiotoxicity risks are highlighted using SHAP (SHapley Additive exPlanations) values. This enhances molecular understanding and facilitates the rational design of safer bioactive compounds. Last but not least, CUPID is freely accessible at https://prometheus.farmacia.uniba.it/cupid.

Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages. To accomplish this task, we have developed web platforms such as TIRESIA and TISBE.Based on a benchmark dataset, TIRESIA employs an explainable artificial intelligence approach combined with SHAP analysis to unveil the molecular features responsible for calculating the developmental toxicity. Descending from TIRESIA, TISBE employs a larger dataset, an explainable artificial intelligence framework based on a fragment-based fingerprint encoding, a consensus classifier, and a new double top-down applicability domain. We report here some practical examples for getting started with TIRESIA and TISBE.

The rational design of adenosine A 2A receptor antagonists offers a non‐dopaminergic approach to alleviate symptoms of Parkinson's disease (PD). Preclinical studies indicate that A 2A antagonists may inhibit neuronal loss, although human studies are essential for validating effectiveness. This research focuses on optimizing ligands for the A 2A receptor through a multifaceted method uniting 3D quantitative structure–activity relationship (QSAR) modeling, molecular docking, binding energy calculations, molecular dynamics (MD) simulations, and interaction analysis. A robust atom‐based 3D‐QSAR model was developed, achieving predictive performance metrics (R 2 = 0.80, Q 2 = 0.65) and identifying key structural features associated with bioactivity. Screening 3,958 compounds, five lead molecules (CHEMBL16687, 113142, 1760901, 4289874, 482436) were prioritized based on binding energies (ranging from −12.938 to −9.986 kcal/mol). Binding affinity confirmations through MMGBSA highlighted significant electrostatic and van der Waals interactions. A 200 ns MD simulation assessed the stability of these compounds, with CHEMBL4289874 showcasing exceptional stability and occupying the smallest phase space in principal component analysis (PCA), indicating superior stability relative to the other compounds. 2D interaction diagrams elucidated critical ligand‐residue interactions fundamental to maintaining structural integrity. This comprehensive investigation positions CHEMBL4289874 as an exceptionally promising candidate for further development in PD treatment.

Endothelial dysfunction, mainly linked to ionic channel malfunctions, is a key mechanism in the pathogenesis of different vascular disorders including hypertension and atherosclerosis. Several studies confirm a strong correlation between endothelial dysfunction, inflammation and oxidative stress. Regular consumption of antioxidant polyphenol-rich foods has been linked to a reduction in cardiovascular morbidity and mortality. The flavone chrysin, a known Cav1.2 channel inhibitor, was selected in this work as lead compound to generate a library of its derivatives evaluated for their antioxidant and anti-inflammatory properties by means of vitro and cell-based assays. Among them, the best one resulted in the 8-nitrochrysin (1i). Molecular docking revealed that the nitro group plays a critical role in enhancing the binding mode toward iNOS by facilitating the formation of additional polar interactions compared to chrysin. LogP values of chrysin derivatives were also experimentally determined via spectrophotometric method to evaluate the impact of the chemical modifications with respect to the parent compound.

Background: In this study, we report a novel series of proline- and pipecolic acid-based small molecules designed as allosteric inhibitors of the NS2B/NS3 serine proteases from dengue and Zika viruses, key targets in antiviral drug discovery. Results: Enzymatic studies revealed that S-proline derivatives bearing electron-withdrawing substituents on the aromatic ring, particularly that with a trifluoromethyl group in meta position (i.e., compound 3, IC50 = 5.0 µM), were the most potent against DENV NS2B/NS3, while nitro-substituted inhibitors were mostly effective only against the ZIKV protease. R-configured pipecolic acid-based derivatives were the only ones active against DENV NS2B/NS3, even if the mid-micromolar range; however, they demonstrated improved cellular efficacy since inhibitors 24 and 27 exhibiting strong activity in a DENV2 protease reporter gene assay (EC50 = 5.2 and 5.1 µM, respectively). All compounds showed no cytotoxicity (CC50 > 100 µM) and were selective for the viral protease over off-target serine proteases. Structure-based approaches were exploited to map the druggable allosteric site close to Asn152. Conclusions: Our findings led us to identify proline and pipecolic acid-based inhibitors as promising leads for the development of selective flaviviral NS2B/NS3 allosteric inhibitors.

Background/Objectives: Studying protein–protein interaction (PPI) networks is crucial in understanding cancer phenotypes and molecular mechanisms. Here, we focus on PPIs involved in 12 different types of cancer (oncoPPIs), highlighting those protein pockets serving as outposts to modulate protein functioning. Methods: To explore these cavities linked to the cancer phenotype changes, we built a comprehensive pocketome of 314 crystallographically solved oncoPPIs. Based on this experimental data, we identified and investigated all ligandable protein pockets by employing 3D geometric and energetic descriptors. These pockets were classified as suitable for designing new oncoPPI modulators or PROTACs. The ligand-bound crystallographic pockets were analyzed to compare their properties across cancer types. Finally, 3D oncoPPI networks were built for each cancer type to identify highly connected proteins acting as hubs. Results: Combining interaction networks with structural pocket data helps identify cancer-relevant proteins and key interacting residues. Using this approach, we present clinical examples (e.g., S100A1, NRP1, CTNNB1, VCP) to show the therapeutic value of targeting ligandable 3D oncoPPIs. We also provide a publicly available reference dataset supporting future research. Conclusions: Notably, this study offers a flexible framework for evaluating and prioritizing novel disease targets.

The present study focused on the design and synthesis of two classes of hydrazones of isatin derivatives bearing morpholine and piperazine units ( IHP1-IHP6 and IHM1-IHM6). The molecules have been further investigated for their in vitro cholinesterases inhibition studies. As per the enzyme inhibition study, IHM2 showed high inhibitory activity against h AChE with an IC50 value of 1.60 +/- 0.51 mu M indicating that substitution of chlorine at C-5 position of isatin ring and presence of morpholine moiety displayed the higher inhibitory activity towards h AChE. The lead molecules were further evaluated for their CNS drug likeness by using PAMPA assay. As from the PAMPA assay, it was demonstrated that IHM2 exhibited significant CNS permeability having Pe value greater than 4.0 x 10-6 cm/s. Docking studies, performed to understand the binding mode of IHM2 with h AChE, resulted in a docking score of-10.250 kcal/mol, with the compound showing hydrogen bonding with Phe 295, Ser 293 and pi-pi stacking interaction with Phe 338. Molecular dynamics simulations indicated a significant stability of IHM2- h AChE complex over a time period of 100 ns. In addition, IHM2 possess favourable ADME properties with goog blood-brain barrier permeability. Overall, lead molecule IHM2 could be a promising AChE inhibitor to treat various neurodegenerative disorders.

The immune response induced by gluten is the result of molecular mechanisms involving gliadin peptides, DQ2 or DQ8 glycoproteins, and the interaction with T lymphocyte receptors. DQ8-glia-α1 is an immunodominant peptide present in gliadin from wheat Triticum spelta that interacts with the DQ8 protein, as proven through transgenic mouse models. The research was carried out by performing a computational analysis aimed at finding antagonistic peptides of the DQ8-glia-α1 peptide, i.e. peptides obtained by varying its amino acids to maintain or even enhance the binding towards DQ8 and at the same time to prevent an immune response by a reduced interaction with the T lymphocyte receptors. Crystallographic structures of DQ8 and three different T-cell receptors were taken as experimental starting systems, the peptide-protein interaction was modelled by molecular dynamics simulations and molecular interaction field calculations, and the optimal mutations of the peptide sequence were identified by using multivariate analysis. The method provided a list of nine immunodominant peptide candidates, which were produced by chemical synthesis and validated by tests on transgenic mice. The results showed that immunization with the peptide (DQ8-glia-α1, designated M1) induced in vitro antigen-specific secretion of IFN-γ restricted to the M1 peptide alone. M1 also stimulated antigen-specific secretion of the regulatory cytokine IL-10. A peptide (i.e., M10) was identified as a potential therapeutic molecule for down-regulating the inflammatory condition triggered by the DQ8-glia-α1 immunodominant peptide in CD.

The main goal of this study is the identification of existing drugs that could be repurposed as antagonists of the V2R, a GPCR controlling renal water balance and involved in abnormal cell proliferation, cancer, and renal cyst enlargement. Given its clinical importance, we carried out the reverse screening of a collection of 1882 existing drugs to repurpose them towards V2R by employing PLATO, a home-built target fishing AI-based platform. Five drugs were shortlisted as promising candidates for V2R: cabergoline, clopidogrel, cloxacillin, perphenazine, and zafirlukast. Renal collecting duct MCD4 cells, stably expressing human V2R and AQP2, were used for experimentally testing the effects of the prioritized drugs on V2R responses. FRET studies were conducted to assess whether these drugs affect the DDAVP-induced cAMP responses. Interestingly, zafirlukast, at single-digit nanomolar concentration significantly reduced the DDAVP-dependent cAMP production and water reabsorption, with effects comparable to tolvaptan, a well-known selective V2R antagonist. The molecular rationale behind the observed binding was explained by mapping on the V2R a molecular cleft superimposable to CysLTR1 binding site of zafirlukast. Induced-fit docking simulations demonstrated that zafirlukast engages V2R by adopting a binding conformation closely resembling that of X-ray solved vasopressin. Taken together, our results support the repurposing of zafirlukast as a promising V2R antagonist candidate.

Powdered silk fibroin (PSF) extracted from Bombyx mori cocoons is reported as a heterogeneous organocatalyst in the selective Michael 1,4 addition of nitromethane to α,β -unsaturated carbonyl compounds, affording Michael adducts in almost quantitative yields, with complete antidiastereoselectivity and in mild conditions. PSF proved to be reusable for more than 50 recycles without any loss of catalytic activity. In silico studies suggest the presence of an enzyme-like pocket as the active catalytic site, pointing out fibroin fibers as a heterogeneous biological organocatalyst.

The whey protein (WP) fraction represents 18–20% of the total milk nitrogen content. It was originally considered a dairy industry waste, but upon its chemical characterization, it was found to be a precious source of bioactive components, growing in popularity as nutritional and functional food ingredients. This has generated a remarkable increase in interest in applications in the different sectors of nutrition, food industry, and pharmaceutics. WPs comprise immunoglobulins and proteins rich in branched and essential amino acids, and peptides endowed with several biological activities (antimicrobial, antihypertensive, antithrombotic, anticancer, antioxidant, opioid, immunomodulatory, and gut microbiota regulation) and technological properties (gelling, water binding, emulsification, and foaming ability). Currently, various process technologies and biotechnological methods are available to recover WPs and convert them into BioActive Peptides (BAPs) for commercial use. Additionally, in silico approaches could have a significant impact on the development of novel foods and/or ingredients and therapeutic agents. This review provides an overview of current and emerging methods for the production, selection, and application of whey peptides, offering insights into bioactivity profiling and potential therapeutic targets. Recent updates in legislation related to commercialized WPs-based products are also presented.

Generative models have revolutionized de novo drug design, allowing to produce molecules on-demand with desired physicochemical and pharmacological properties. String based molecular representations, such as SMILES (Simplified Molecular Input Line Entry System) and SELFIES (Self-Referencing Embedded Strings), have played a pivotal role in the success of generative approaches, thanks to their capacity to encode atom- and bond- information and ease-of-generation. However, such ‘atom-level’ string representations could have certain limitations, in terms of capturing information on chirality, and synthetic accessibility of the corresponding designs. In this paper, we present fragSMILES, a novel fragment-based molecular representation in the form of string. fragSMILES encode fragments in a ‘chemically-meaningful’ way via a novel graph-reduction approach, allowing to obtain an efficient, interpretable, and expressive molecular representation, which also avoids fragment redundancy. fragSMILES contributes to the field of fragment-based representation, by reporting fragments and their ‘breaking’ bonds independently. Moreover, fragSMILES also embeds information of molecular chirality, thereby overcoming known limitations of existing string notations. When compared with SMILES, SELFIES and t-SMILES for de novo design, the fragSMILES notation showed its promise in generating molecules with desirable biochemical and scaffolds properties. Molecular representations based on SMILES strings play a pivotal role in the success of generative approaches for de novo design, however capturing information on synthetic accessibility remains challenging. Here, the authors report fragSMILES as a fragment-based molecular representation in strings that embed chemical information and molecular chirality, showing promise in generating molecules with desirable properties.

INTRODUCTION:The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED:This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION:The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.