AI Medical Compendium Topic:
Molecular Docking Simulation

Clear Filters Showing 451 to 460 of 597 articles

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

Scientific reports
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative ...

Design and synthesis of new phthalazine-based derivatives as potential EGFR inhibitors for the treatment of hepatocellular carcinoma.

Bioorganic chemistry
Searching for new leads in the battle of cancer will never ends, we herein disclose the design and synthesis of new phthalazine derivatives and their in vitro and in vivo testing for their antiproliferative activity. Phthalazine was selected as a pri...

Albumin binding, anticancer and antibacterial properties of synthesized zero valent iron nanoparticles.

International journal of nanomedicine
BACKGROUND: Nanoparticles (NPs) have been emerging as potential players in modern medicine with clinical applications ranging from therapeutic purposes to antimicrobial agents. However, before applications in medical agents, some in vitro studies sho...

Gene ontology improves template selection in comparative protein docking.

Proteins
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and a...

Practical Model Selection for Prospective Virtual Screening.

Journal of chemical information and modeling
Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of liga...

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data.

Journal of chemical information and modeling
Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) wi...

The Rational Discovery of a Tau Aggregation Inhibitor.

Biochemistry
Intrinsically disordered proteins play vital roles in biology, and their dysfunction contributes to many major disease states. These proteins remain challenging targets for rational ligand discovery or drug design because they are highly dynamic and ...

Integration of virtual screening and susceptibility test to discover active-site subpocket-specific biogenic inhibitors of Helicobacter pylori shikimate dehydrogenase.

International microbiology : the official journal of the Spanish Society for Microbiology
Shikimate dehydrogenase (HpSDH) (EC 1.1.1.25) is a key enzyme in the shikimate pathway of Helicobacter pylori (H. pylori), which catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. Targeting HpSDH has been recognize...