Ligandscout Price

[Silvana Fleischacker]

Accordingto the information found in the Availability section of the product's website, LigandScout is priced individually. This means that you're going to need to send an email to the application's sale department and get an official answer from them.

In the meantime, you can download a trial version of the application after you register on the website. In case the trial period has expired, you can extend it by contacting the company's support team.

You can download the application through the link posted below. The version available for testing is 3.2, and if you want the 4.0 version, then you will have to contact the developers at the following email address: [email protected]

You will be informed about the price after you get in touch with the developer. He is the only one who knows the price. I have checked the official website, but no information about the price is provided. However, you have the possibility to download a trial version of the application.

CD ComputaBio, a reliable computational biology service provider located in New York, is always hammering away at research and trials in order to provide customers with access to the latest software, technologies, and expertise at a competitive price and fast turnaround time. The company recently announced the launch of ligand-based pharmacophore model service for scientists to accelerate the drug candidate development process.

Molecular docking technology remains the most popular structure-based drug design method that takes full advantage of protein-ligand interaction information. However, in virtual screening, compared with molecular docking, pharmacophore-based methods show significant advantages in terms of computational cost and accuracy. The docking-based virtual screening method showed a higher false positive rate. Therefore, combining complementary ligand-based methods and receptor-based methods helps to improve the reliability of the method.

Studies have shown that in virtual screening, ligand-based pharmacophore models are very successful in the discovery of new active molecules. In addition, the pharmacophore is also used in the molecular docking procedure to better distinguish the wrong conformation from the correct conformation, thereby improving the success rate of molecular docking.

CD ComputaBio provides Ligandscout for pharmacophore modeling and virtual screening. In addition to structure-based design, it is also good at ligand-based design: starting from a series of compounds with similar binding patterns to identify and identify pharmacophore models. The input file of Ligandscout contains: training set compounds (active compounds, inactive compounds) and test set compounds (active compounds, inactive compounds), training set compounds are used to generate pharmacophore models, test set compounds continue to multi-pharmacophore models scoring, and finally output up to 10 models with scoring.

CD ComputaBio has rich practical experience and core technology in pharmacophore model construction research. In addition to ligand-based pharmacophore model service, it also provides the following services to facilitate your drug development process:

(1)Pharmacophore Model Service Without Protein Structure and Without Ligand Structure

(2)Fragment-based Drug Design

(3)Receptor-based Pharmacophore Model Service

(4)Multiple Targeting Design

About CD ComputaBio

With years of experience, CD ComputaBio can provide customers with professional computational biology services. Utilizing rich experience and powerful technology in computational science, the company can provide customers with many computational biology analysis services such as molecular dynamics simulation, drug design, virtual screening, quantum chemical calculations, etc.

Data mining is an important step for preparing and/or analyzing screening data. ChemBioFrance offers a portal to in-house developed and maintained databases, thereby enabling simple queries regarding tour molecules/targets of interest.

Virtual screening of compound libraries is apotent tool, enabling a low-cost selection of commercially available compounds(10-10000) for experimental validation. Many properties can be used as filtersto retrieve potentially interesting hits: predicted binding mode/affinity to atarget protein, similarity to a known active compound, peculiar physicochemicalproperties. ChemBioFrance has access to a catalogue of ca. 5 million commercialcompounds immediately available for in silico/experimental screening.

Proteinstructure-based screening: After preparation of the 3D structure (x-raycoordinates, homology model), the ChemBioFrance library is either docked to abinding site of interest or analyzed for its complementarity to aprotein-ligand interaction pharmacophore. Hits are selected according to the user'sneeds: e.g., presence of key interactions with key residues, binding freeenergy, ligand efficiency). A list of commercially available hits (catalog id,supplier, price) is transmitted to the client for purchase

Ligand-basedscreening: The compound library is screened for 2D/3D similarity to one ofseveral known actives using QSAR/QSPR models, SOM/GTM maps. A list of hits isgiven to the client pour purchase and experimental validation.

Biologicsrepresent a promising alternative to small molecular weight compounds for thedevelopment of new drugs. Among them, peptides are a specific class ofcompounds involved in cellular signaling and trafficking, can presentantibiotic activities or modulate protein-protein interactions. Recent progresshave been noticed in the control of their bioavailability (resistance toenzymatic degradation), biodistribution (various administration routes,targeting intracellular proteins or nucleic acids, targeting of specific celllines) and production cost. More than 60 peptides are currently under clinicaldevelopment. ChemBioFrance offers the possibility to identify, characterize andoptimize interfering peptides, i.e. peptides able to modulate a specific protein-proteininteraction.

This approach requires the structure of the target protein and thesequence of the interfering peptides. Timelines are ca. 1 month for a solubletarget and to be defined on a case-by-case basis for a membrane receptor.

o Cellularinternalization: coupling of the peptide to a cell-penetrating sequence. Proofof concept of internalization can be realized by fluorescence spectroscopy.Costs and timelines depends on the project

Despite the increasing number of protein-protein interaction (PPI)inhibitors, the success rate of experimental PPI screening remains low, notablybecause of the inadequacy of screening collections. ChemBioFrance hasundertaken a major effort in designing compound libraries specifically focusedon protein-protein interfaces.

In-house developed machine learning methods2P2IHUNTER and PPI-HitProfiler have been applied to compoundlibraries from two suppliers (Molport and Ambinter) registering 6.3 and 5.7million compounds, respectively. Merging the two sources and filteringcompounds for PAINS, aggregators frequent hitters, and subsequently for ADMETparameters led to a unique set of 10,314 compounds that have been plated andare currently available for in silico or experimental screening.

theselection of predicted PPI modulators among a proprietary compound collection.Hits are selected in agreement with the client (e.g. presence of necessaryfunctional groups, 2D or 3D similarity to known actives). A list ofcommercially available compounds (catalog number, supplier, and price) can bedelivered if necessary.

ADMET filtering of compound libraries enables the removal or annotationof compounds with potential liabilities with respect to a clinical developmentor identification of a new molecular probe. This analysis can be done after avirtual/experimental screen in order to gain knowledge before or after a hit tolead optimization stage. Many types of filters have beenimplemented and can be fine-tuned according to the user's need.

Filteringconsists in a computation of several above-described properties and aniterative process to remove compounds satisfying queries and structural alerts.Compounds passing the filters, and those rejected are separately stored intables and structural files.

Identifying the main target of a phenotypicscreen remains a tricky endeavour. Moreover, it might be interesting to knowsecondary off-targets of any bioactive molecule. ChemBioFrance offers an in silico approach to target predictionfrom the simple knowledge of a ligand structure.

A target library of 4,500 proteins ofpharmaceutical interest (GPCRs, nuclear receptors, ion channels, kinases,proteases; etc...) is screened according to a proprietary method (Profiler) [1] using machine learningalgorithms (support vector machines, random forests) specific for each of theinvestigated target. According to the current knowledge on the target and itsknown ligands. For each compound to be investigated, Profiler defines a list ofpotential targets with predicted inhibition constants. When applied to 189clinical candidates, the method was able to recover the main target among ashort list (usually 15-20 targets) in 87% of cases. Profiler has also been appliedto the identification of secondary targets with further experimental validation[1].

By exploiting several public kinase inhibitorprofiling datasets, we have developped robust chemogenomics statistical models,also called proteochemometrics PCM, to predict the selectivity profile of novelkinase inhibitors (2). The tool uses 2D and 3D molecular descriptors and takesinto account the different conformations, active and inactive, of proteinkinases. Three different machine learning algorithms wereevaluated: Nave Bayes (NB), Support Vector Machines (SVM) and Random Forest(RF).

Prediction of physicochemical and/or biologicalproperties can be realized by QSAR methods including machine learning and deplearning. The QSAR approach relies on the description of molecular structuresby an ensemble of data called molecular descriptors. These moleculardescriptors and then linked to any property of interest thanks to amathematical model whose parameters may be deduced from learning algorithms.Deep learning can be perceived as an evolution of classical QSAR techniques intwo manners: (i) by trying to get rid of molecular descriptors by directlyconsidering molecular graphs, (ii) targeting complex properties like spectra orimages. In all cases, models are prepared and validated according to rigorousprotocols aimed at estimating model performance and applicability domain. Deeplearning methods can also directly generate new chemical structures with thedesired properties.

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