132 research outputs found
Application of virtual screening to the discovery of novel nicotinamide phosphoribosyltransferase (NAMPT) inhibitors with potential for the treatment of cancer and axonopathies.
NAMPT may represent a novel target for drug discovery in various therapeutic areas, including oncology and inflammation. Additionally, recent work has suggested that targeting NAMPT has potential in treating axon degeneration. In this work, publicly available X-ray co-crystal structures of NAMPT and the structures of two known NAMPT inhibitors were used as the basis for a structure- and ligand-based virtual screening campaign. From this, two novel series of NAMPT inhibitors were identified, one of which showed a statistically significant protective effect when tested in a cellular model of axon degeneration
Wanted: unique names for unique atom positions. PDB-wide analysis of diastereotopic atom names of small molecules containing diphosphate
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p
AutoClickChem: Click Chemistry in Silico
Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu
An effective docking strategy for virtual screening based on multi-objective optimization algorithm
Cell-active Small Molecule Inhibitors of the DNA-damage Repair Enzyme Poly(ADP-ribose) Glycohydrolase (PARG) : Discovery and Optimization of Orally Bioavailable Quinazolinedione Sulfonamides
DNA
damage repair enzymes are promising targets in the development
of new therapeutic agents for a wide range of cancers and potentially
other diseases. The enzyme poly(ADP-ribose) glycohydrolase (PARG)
plays a pivotal role in the regulation of DNA repair mechanisms; however,
the lack of potent drug-like inhibitors for use in cellular and in
vivo models has limited the investigation of its potential as a novel
therapeutic target. Using the crystal structure of human PARG in complex
with the weakly active and cytotoxic anthraquinone <b>8a</b>, novel quinazolinedione sulfonamides PARG inhibitors have been identified
by means of structure-based virtual screening and library design.
1-Oxetan-3-ylmethyl derivatives <b>33d</b> and <b>35d</b> were selected for preliminary investigations in vivo. X-ray crystal
structures help rationalize the observed structure–activity
relationships of these novel inhibitors
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques
Structure‐guided screening of chemical database to identify NS3‐NS2B inhibitors for effective therapeutic application in dengue infection
A Combined Quantum Mechanical/Molecular Mechanical Study of Catalysis by the Enzymes Phospholipase A_2 and Thermolysin
360 Large-scale
virtual screening for discovering leads in the postgenomic era Virtual screening, or in silico screening, is a new approach attracting increasing levels of interest in the pharmaceutical industry as a productive and cost-effective technology in the search for novel lead compounds. Although the principles involved—the computational analysis of chemical databases to identify compounds appropriate for a given biological receptor—have been pursued for several years in molecular modeling groups, the availability of inexpensive high-performance computing platforms has transformed the process so that increasingly complex and more accurate analyses can be performed on very large data sets. The virtual screening technology of Protherics Molecular Design Ltd. is based on its integrated software environment for receptorbased drug design, called Prometheus. In particular, molecular docking is used to predict the binding modes and binding affinities of every compound in the data set to a given biological receptor. This method represents a very detailed and relevant basis for prioritizing compounds for biological screening. This paper discusses the broader scope of virtual screening and, as an example, describes our recent work in docking one million compounds into the estrogen hormone receptor in order to highlight the technical feasibility of performing very largescale virtual screening as a route to identifying novel drug leads. Drug discovery has traditionally made progress by a combination of random screening and rational design. 1 In practice, the latter approach is often frustrated by a paucity of experimental data that define the structure and properties of the biological target. Today, this situation is starting to change, with initiatives such as the Human Genome Project promising access to a vast amount of data detailing the molecular basis of disease. 2 In parallel we have wit
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