1,721,109 research outputs found
Computational Approaches for Drug Discovery
Full text linkComputational approaches represent valuable and essential tools in each step of the drug discovery and development trajectory [...]
Artificial Intelligence in Translational Medicine
No full textThe huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field
Organic Isothiocyanates as Hydrogen Sulfide Donors
No full textSignificance: Hydrogen sulfide (H2S), the "new entry" in the series of endogenous gasotransmitters, plays a fundamental role in regulating the biological functions of various organs and systems. Consequently, the lack of adequate levels of H2S may represent the etiopathogenetic factor of multiple pathological alterations. In these diseases, the use of H2S donors represents a precious and innovative opportunity. Recent Advances: Natural isothiocyanates (ITCs), sulfur compounds typical of some botanical species, have long been investigated because of their intriguing pharmacological profile. Recently, the ITC moiety has been proposed as a new H2S-donor chemotype (with a l-cysteine-mediated reaction). Based on this recent discovery, we can clearly observe that almost all the effects of natural ITCs can be explained by the H2S release. Consistently, the ITC function was also used as an original H2S-releasing moiety for the design of synthetic H2S donors and original "pharmacological hybrids." Very recently, the chemical mechanism of H2S release, resulting from the reaction between l-cysteine and some ITCs, has been elucidated. Critical Issues: Available literature gives convincing demonstration that H2S is the real player in ITC pharmacology. Further, countless studies have been carried out on natural ITCs, but this versatile moiety has been used only rarely for the design of synthetic H2S donors with optimal drug-like properties. Future Directions: The development of more ITC-based synthetic H2S donors with optimal drug-like properties and selectivity toward specific tissues/pathologies seem to represent a stimulating and indispensable prospect of future experimental activities
3D-QSAR using pharmacophore-based alignment and virtual screening for discovery of novel MCF-7 cell line inhibitors
No full textThe development of a novel approach for the prediction of antiestrogenic activity is described, bringing up to date a previous pharmacophore study. Software Phase has been used to derive a 3D-QSAR model based, as alignment rule, on a pharmacophore built on three compounds highly active against MCF-7 cell line. Five features comprised the pharmacophore: two hydrogen-bond acceptors, one hydrogen-bond donor, and two aromatic rings. The sequential 3D-QSAR yielded a test set q(2) equal to 0.73 and proved to be predictive with respect to an external test set of 21 compounds (r(2) = 0.69). The model was used to detect new MCF-7 inhibitors through 3D-database searching and identified fourteen compounds that were subsequently tested in vitro against the MCF-7 human breast adenocarcinoma cell line. Eleven out of the fourteen compounds exhibited inhibitory activity with IC50 values ranging between 30 and 186 μM. The results of the study confirmed the fundamental validity of the chosen approach as a hit discovery tool
Pharmacophore modeling: a continuously evolving tool for computational drug design
No full textIn the latest two or three years progressive
applications of pharmacophore modeling continue to
appear in literature. Pharmacophore based parallel
screening, for instance, has been introduced in 2006.
Moreover, in 2008, a survey discussing the prospective
impact of virtual screening techniques in the
discovery of bioactive natural products has been
published. Finally, virtual screening techniques
from the drug discovery field are beginning to
be used for profiling the bioactivity of chemicals (especially those of potential environmental concern) with the aim of
prioritizing compounds for further testing using more complex systems and
reducing and ultimately replacing the use of animals in regulatory testing.
Pharmacophore modeling might be extremely helpful to allow full
achievement of all the above mentioned goals. In this contribution we report
a couple of case studies where pharmacophore generation and handling
played a pivotal role. In particular, in the first example, the development of a
novel computational pre-screening approach to be used as an in silico
filtering tool for natural products is described, applied to the estrogen
receptor-α subtype. In the second study, differently, the validation of a
preexisting pharmacophore by the prediction of the antifungal activities of
new azole compounds is discussed. In this case, it comes to light the
importance and utility of adding excluded volumes to a pharmacophore, to
increase its predictivity
Amyloid β fibril disruption by oleuropein aglycone:long-time molecular dynamics simulation to gain insight into the mechanism of action of this polyphenol from extra virgin olive oil
No full textIn the central nervous system (CNS), extra virgin olive oil (EVOO) produces interesting effects against neurodegenerative disorders including Alzheimer's disease (AD). The valuable properties of EVOO are largely ascribed to oleuropein aglycone (OA), its most abundant phenolic constituent. In particular, it has been demonstrated that in AD, OA produces strong neuroprotective effects being able to reduce amyloid ß (Aß) aggregates, thereby diminishing the related cytotoxicity and inflammation. OA prevents Aß aggregation, but more importantly OA was able to disrupt the preformed Aß fibrils. Herein, we describe a comprehensive computational investigation of the mechanism of action of OA as an Aß fibril disruptor at the molecular level. We employed extensive molecular docking calculations and long-time molecular dynamics simulation for mimicking the system of OA/Aß fibrils. The results showed that OA is able to move in depth within the Aß fibrils targeting a key motif in Aß peptide, known to be relevant for stabilizing the assembled fibrils. OA causes a structural instability of preformed Aß fibrils, determining the effective Aß fibril disaggregation. Accordingly, this study highlighted the role of OA as a potent anti-amyloidogenic drug. On the other hand, our work has relevant implications for rationally designing potent multifunctional compounds acting as disease modifying anti-Alzheimer's drugs for the development of innovative anti-AD therapeutics.</p
Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening
No full textHistone deacetylases (HDACs) as an important family of epigenetic regulatory enzymes are implicated in the onset and progression of carcinomas. As a result, HDAC inhibition has been proven as a compelling strategy for reversing the aberrant epigenetic changes associated with cancer. However, non-selective profile of most developed HDAC inhibitors (HDACIs) leads to the occurrence of various side effects, limiting their clinical utility. This evidence provides a solid ground for ongoing research aimed at identifying isoform-selective inhibitors. Among the isoforms, HDAC1 have particularly gained increased attention as a preferred target for the design of selective HDACIs. Accordingly, in this paper, we have developed a reliable virtual screening process, combining different ligand- and structure-based methods, to identify novel benzamide-based analogs with potential HDAC1 inhibitory activity. For this purpose, a focused library of 736,160 compounds from PubChem database was first compiled based on 80% structural similarity with four known benzamide-based HDAC1 inhibitors, Mocetinostat, Entinostat, Tacedinaline, and Chidamide. Our inclusive in-house 3D-QSAR model, derived from pharmacophorebased alignment, was then employed as a 3D-query to discriminate hits with the highest predicted HDAC1 inhibitory activity. The selected hits were subjected to subsequent structure-based approaches (induced-fit docking (IFD), MM-GBSA calculations and molecular dynamics (MD) simulation) to retrieve potential compounds with the highest binding affinity for HDAC1 active site. Additionally, in silico ADMET properties and PAINS filtration were also considered for selecting an enriched set of the best drug-like molecules. Finally, six top-ranked hit molecules, CID_38265326, CID_56064109, CID_8136932, CID_55802151, CID_133901641 and CID_18150975 were identified to expose the best stability profiles and binding mode in the HDAC1 active site. The IFD and MD results cooperatively confirmed the interactions of the promising selected hits with critical residues within HDAC1 active site. In summary, the presented computational approach can provide a set of guidelines for the further development of improved benzamide-based derivatives targeting HDAC1 isoform
An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction
No full textThe p53 protein, also called guardian of the genome, plays a critical role in the cell cycle regulation and apoptosis. This protein is frequently inactivated in several types of human cancer by abnormally high levels of its negative regulator, mouse double minute 2 (MDM2). As a result, restoration of p53 function by inhibiting p53-MDM2 protein-protein interaction has been pursued as a compelling strategy for cancer therapy. To date, a limited number of small-molecules have been reported as effective p53-MDM2 inhibitors. X-ray structures of MDM2 in complex with some ligands are available in Protein Data Bank and herein, these data have been exploited to efficiently identify new p53-MDM2 interaction antagonists through a hierarchical virtual screening strategy. For this purpose, the first step was aimed at compiling a focused library of 686,630 structurally suitable compounds, from PubChem database, similar to two known effective inhibitors, Nutlin-3a and DP222669. These compounds were subjected to the subsequent structure-based approaches (quantum polarized ligand docking and molecular dynamics simulation) to select potential compounds with highest binding affinity for MDM2 protein. Additionally, ligand binding energy, ADMET properties and PAINS analysis were also considered as filtering criteria for selecting the most promising drug-like molecules. On the basis of these analyses, three top-ranked hit molecules, CID_118439641, CID_60452010 and CID_3106907, were found to have acceptable pharmacokinetics properties along with superior in silico inhibitory ability towards the p53-MDM2 interaction compared to known inhibitors. Molecular docking and molecular dynamics results well confirmed the interactions of the final selected compounds with critical residues within p53 binding site on the MDM2 hydrophobic clefts with satisfactory thermodynamics stability. Consequently, the new final scaffolds identified by the presented computational approach could offer a set of guidelines for designing promising anti-cancer agents targeting p53-MDM2 interaction
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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