Search Results for "pharmacophore"
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Showing 5 open source projects for "pharmacophore"
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Whether natural disaster, cyberattack, or plain-old human error, data can disappear in the blink of an eye. ConnectWise BCDR (formerly Recover) delivers reliable and secure backup and disaster recovery backed by powerful automation and a 24/7 NOC to get your clients back to work in minutes, not days.
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Repfabric is a customer relationship management (CRM) software designed specifically for multi-line sales teams (i.e. reps, distributors, wholesalers, dealers, and manufacturers). It streamlines and simplifies the sales process by providing deep integration with email, contacts, calendars, and deal tracking. The platform enables users to track commissions from CRM to sale, make updates directly from mobile devices, and document sales calls using voice-to-text features.
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Fast and efficient 3D pharmacophore search developed by the Camacho Lab (http://smoothdock.ccbb.pitt.edu) at the University of Pittsburgh. Source code is available through the svn repository.
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Open3DQSAR is a free, open-source tool written in C aimed at pharmacophore exploration by high-throughput chemometric analysis of molecular interaction fields (MIFs).
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QSAR Qualitative Validation
Qualitative validation of QSAR models
The purpose of this program is to verify the ability of the QSAR/Pharmacophore model to distinguish significantly between the two classes (i.e. active and inactive molecules). It is based on calculation of different qualitative validation parameters such as sensitivity, specificity, precision, accuracy, and F-measure. -
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CDL provides a generic C++ framework to write algorithms for the calculation of molecular descriptors. CDL provides efficient substructure search, fingerprints and pharmacophore algorithms, and many more for the calculation molecular descriptors.
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New customers get $300 in free credits to spend on Speech-to-Text. All customers get 60 minutes for transcribing and analyzing audio free per month, not charged against your credits.
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Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.
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