Search Results (27)

Background: Pancreatic lipase (PL), the principal enzyme catalyzing the hydrolysis of dietary triacylglycerols in the intestinal lumen, is pivotal for efficient lipid absorption and plays a central role in metabolic homeostasis. Enhanced PL activity promotes excessive lipid assimilation and contributes to positive energy balance, key pathophysiological mechanisms underlying the escalating global prevalence of obesity—a complex, multifactorial condition strongly associated with metabolic disorders, including type 2 diabetes mellitus and cardiovascular disease. Inhibition of pancreatic lipase (PL) constitutes a well-established therapeutic approach for attenuating dietary lipid absorption and mitigating obesity. Methods: With the aim to identify putative PL inhibitors, a Structure-Based Virtual Screening (SBVS) of PhytoHub database naturally occurring derivatives was performed. A refined library of 10,404 phytochemicals was virtually screened against a crystal structure of pancreatic lipase. Candidates were filtered out based on binding affinity, Lipinski’s Rule of Five, and structural clustering, resulting in six lead compounds. Results: In vitro, enzymatic assays confirmed theoretical suggestions, highlighting Pinoresinol as the best PL inhibitor. Molecular dynamics simulations, performed to investigate the stability of protein–ligand complexes, revealed key interactions, such as persistent hydrogen bonding to catalytic residues. Conclusions: This integrative computational–experimental workflow highlighted new promising natural PL inhibitors, laying the foundation for future development of safe, plant-derived anti-obesity therapeutics. Full article

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation. Full article

Chromatin organization, which is under the control of histone deacetylases (HDACs), is frequently deregulated in cancer cells. Amongst HDACs, HDAC8 plays an oncogenic role in different neoplasias by acting on both histone and non-histone substrates. Promising anti-cancer strategies have exploited dual-targeting drugs that inhibit both HDAC8 and tubulin. These drugs have shown the potential to enhance the outcome of anti-cancer treatments by simultaneously targeting multiple pathways critical to disease onset and progression. In this study, a structure-based virtual screening (SBVS) of 96403 natural compounds was performed towards the four Class I HDAC isoforms and tubulin. Using molecular docking and molecular dynamics simulations (MDs), we identified two molecules that could selectively interact with HDAC8 and tubulin. CNP0112925 (arundinin), bearing a polyphenolic structure, was confirmed to inhibit HDAC8 activity and tubulin organization, affecting breast cancer cell viability and triggering mitochondrial superoxide production and apoptosis. Full article

Background/Objectives: The development of therapies targeting unregulated Src signaling through selective kinase inhibition using small-molecule inhibitors presents a significant challenge for the scientific community. Among these inhibitors, pyrazolo[3,4-d]pyrimidine heterocycles have emerged as potent agents; however, their clinical application is hindered by low solubility in water. To overcome this limitation, some carrier systems, such as halloysite nanotubes (HNTs), can be used. Methods: Herein, we report the development of HNT-based nanomaterials as carriers for pyrazolo[3,4-d]pyrimidine molecules. To achieve this objective, the clay was modified by two different approaches: supramolecular loading into the HNT lumen and covalent grafting onto the HNT external surface. The resulting nanomaterials were extensively characterized, and their morphology was imaged by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). In addition, the kinetic release of the molecules supramolecularly loaded into the HNTs was also evaluated. QSAR studies were conducted to elucidate the physicochemical and pharmacokinetic properties of these inhibitors, and structure-based virtual screening (SBVS) was performed to analyze their binding poses in protein kinases implicated in cancer. Results: The characterization methods demonstrate successful encapsulation of the drugs and the release properties under physiological conditions. Furthermore, QSAR studies and SBVS provide valuable insights into the physicochemical, pharmacokinetic, and binding properties of these inhibitors, reinforcing their potential efficacy. Conclusions: The cytotoxicity of these halloysite-based nanomaterials, and of pure molecules for comparison, was tested on RT112, UMUC3, and PC3 cancer cell lines, demonstrating their potential as effective agents for prostate and bladder cancer treatment. Full article

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a fast-spreading viral pathogen and poses a serious threat to human health. New SARS-CoV-2 variants have been arising worldwide; therefore, is necessary to explore more therapeutic options. The interaction of the viral spike (S) protein with the angiotensin-converting enzyme 2 (ACE2) host receptor is an attractive drug target to prevent the infection via the inhibition of virus cell entry. In this study, Ligand- and Structure-Based Virtual Screening (LBVS and SBVS) was performed to propose potential inhibitors capable of blocking the S receptor-binding domain (RBD) and ACE2 interaction. The best five lead compounds were confirmed as inhibitors through ELISA-based enzyme assays. The docking studies and molecular dynamic (MD) simulations of the selected compounds maintained the molecular interaction and stability (RMSD fluctuations less than 5 Å) with key residues of the S protein. The compounds DRI-1, DRI-2, DRI-3, DRI-4, and DRI-5 efficiently block the interaction between the SARS-CoV-2 spike protein and receptor ACE2 (from 69.90 to 99.65% of inhibition) at 50 µM. The most potent inhibitors were DRI-2 (IC₅₀ = 8.8 µM) and DRI-3 (IC₅₀ = 2.1 µM) and have an acceptable profile of cytotoxicity (CC₅₀ > 90 µM). Therefore, these compounds could be good candidates for further SARS-CoV-2 preclinical experiments. Full article

Protein hydrolysates from sea cucumber (Apostichopus japonicus) gonads are rich in active materials with remarkable angiotensin-converting enzyme (ACE) inhibitory activity. Alcalase was used to hydrolyze sea cucumber gonads, and the hydrolysate was separated by the ultrafiltration membrane to produce a low-molecular-weight peptide component (less than 3 kDa) with good ACE inhibitory activity. The peptide component (less than 3 kDa) was isolated and purified using a combination method of ACE gel affinity chromatography and reverse high-performance liquid chromatography. The purified fractions were identified by liquid chromatography–tandem mass spectrometry (LC–MS/MS), and the resulting products were filtered using structure-based virtual screening (SBVS) to obtain 20 peptides. Of those, three noncompetitive inhibitory peptides (DDQIHIF with an IC₅₀ value of 333.5 μmol·L⁻¹, HDWWKER with an IC₅₀ value of 583.6 μmol·L⁻¹, and THDWWKER with an IC₅₀ value of 1291.8 μmol·L⁻¹) were further investigated based on their favorable pharmacochemical properties and ACE inhibitory activity. Molecular docking studies indicated that the three peptides were entirely enclosed within the ACE protein cavity, improving the overall stability of the complex through interaction forces with the ACE active site. The total free binding energies (ΔG_total) for DDQIHIF, HDWWKER, and THDWWKER were −21.9 Kcal·mol⁻¹, −71.6 Kcal·mol⁻¹, and −69.1 Kcal·mol⁻¹, respectively. Furthermore, a short-term assay of antihypertensive activity in spontaneously hypertensive rats (SHRs) revealed that HDWWKER could significantly decrease the systolic blood pressure (SBP) of SHRs after intravenous administration. The results showed that based on the better antihypertensive activity of the peptide in SHRs, the feasibility of targeted affinity purification and computer-aided drug discovery (CADD) for the efficient screening and preparation of ACE inhibitory peptide was verified, which provided a new idea of modern drug development method for clinical use. Full article

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein–protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation). Full article

The “Long-COVID syndrome” has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<−6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome. Full article

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset. Full article

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties. Full article

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors. Full article

Sirtuin isoform 2 (SIRT2) is one of the seven sirtuin isoforms present in humans, being classified as class III histone deacetylases (HDACs). Based on the high sequence similarity among SIRTs, the identification of isoform selective modulators represents a challenging task, especially for the high conservation observed in the catalytic site. Efforts in rationalizing selectivity based on key residues belonging to the SIRT2 enzyme were accompanied in 2015 by the publication of the first X-ray crystallographic structure of the potent and selective SIRT2 inhibitor SirReal2. The subsequent studies led to different experimental data regarding this protein in complex with further different chemo-types as SIRT2 inhibitors. Herein, we reported preliminary Structure-Based Virtual Screening (SBVS) studies using a commercially available library of compounds to identify novel scaffolds for the design of new SIRT2 inhibitors. Biochemical assays involving five selected compounds allowed us to highlight the most effective chemical features supporting the observed SIRT2 inhibitory ability. This information guided the following in silico evaluation and in vitro testing of further compounds from in-house libraries of pyrazolo-pyrimidine derivatives towards novel SIRT2 inhibitors (1–5). The final results indicated the effectiveness of this scaffold for the design of promising and selective SIRT2 inhibitors, featuring the highest inhibition among the tested compounds, and validating the applied strategy. Full article

Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process. Full article

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M^pro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M^pro inhibitors against SARS-CoV-2. The 33.8 kDa M^pro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2’s protease (M^pro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M^pro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes’ stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation. Full article

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns. Full article