Computational Resource for Drug Discovery

Source: Wikipedia, the free encyclopedia.

Computational Resources for Drug Discovery (CRDD) is an important module of the

ADME-Tox
properties of molecules. One of the major objectives of CRDD is to promote
open source software in the field of cheminformatics and pharmacoinformatics
.

Features

Under CRDD, numerous resources related to computer-aided drug design have been collected and compiled. These resources are organized and presented on CRDD so users may locate resources from a single source.

Community contribution

CRDD developed a platform where the community may contribute to the process of drug discovery.

Indigenous development: software and web services

Beside collecting and compiling resources, CRDD members develop new software and web services. All services developed are free for academic use. The following are a few major tools developed at CRDD.[citation needed]

Development of databases

Software developed

Resources created

Web services for cheminformatics

CRDD developed an open source platform which allows users to predict inhibitors against novel M. Tuberculosis drug targets and other important properties of drug molecules like ADMET. Following are list of few servers.

  • MetaPred: A webserver for the prediction of cytochrome P450 isoforms responsible for metabolizing a drug molecule. The MetaPred server predicts metabolizing CYP isoforms of a drug molecule/substrate based on SVM models developed using CDK descriptors.[jargon] This server is intended to help researchers working in the field of drug discovery. The effort also demonstrates that it is possible to develop free web servers in the field of cheminformatics. This may encourage other researchers to develop web servers for public use, leading to decreased cost of discovering new drug molecules.[11]
  • ToxiPred: A server for prediction of aqueous toxicity of small chemical molecules in T. pyriformis.
  • KetoDrug: A user friendly web server for binding affinity prediction of ketoxazole derivatives and small chemical molecules against
    Fatty Acid Amide Hydrolase
    (FAAH).
  • KiDoQ: A web server to serve researchers working in the field of designing inhibitors against dihydrodipicolinate synthase (DHDPS), a potential drug target enzyme of a unique bacterial DAP/Lysine pathway.[12]
  • GDoQ: GDoQ (Prediction of GLMU inhibitors using QSAR and AutoDock) is an open source platform for predicting inhibitors against Mycobacterium tuberculosis (M.Tb) drug target N-acetylglucosamine-1-phosphate uridyltransferase (GLMU) protein. This is a potential drug target involved in bacterial cell wall synthesis. This server uses molecular docking and QSAR strategies to predict inhibitory activity value (IC50) of chemical compounds for GLMU protein.[13]
  • ROCR: The ROCR is an R package for evaluating and visualizing classifier performance. It is a flexible tool for creating ROC graphs, sensitivity/specificity curves, area under curve and precision/recall curve. The parametrization can be visualized by coloring the curve according to cutoff.
  • WebCDK: A web interface for the CDK library which is used for predicting descriptors of chemicals.
  • Pharmacokinetics: This data analysis determines the relationship between the dosing regimen and the body's exposure to the drug as measured by the drug's nonlinear concentration time curve. It includes a function to calculate area under this curve. It also includes functions for half-life estimation for a biexponential model, and a two phase linear regression.

Prediction and analysis of drug targets

  • RNApred: Prediction of RNA binding proteins from its amino acid sequence.[14]
  • ProPrint: Prediction of interaction between proteins from their amino acid sequence.[15]
  • DomPrint: A domain-domain interaction (DDI) prediction server.
  • MycoPrint: A web interface for exploration of the interactome of Mycobacterium tuberculosis H37Rv (Mtb) predicted by the "Domain Interaction Mapping" (DIM) method.
  • ATPint: A server for predicting ATP interacting residues in proteins.[16]
  • FADpred: Identification of FAD interacting residues in proteins.[17]
  • GTPbinder: Prediction of protein GTP interacting residues.[18]
  • NADbinder: Prediction of NAD binding residues in proteins.[19]
  • PreMier: Software for predicting mannose interacting residues in proteins.[20]
  • DMAP: Designing of mutants of antibacterial peptides.
  • icaars: Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains. [21]
  • CBtope: Prediction of conformational B-cell epitope in a sequence from its amino acid sequence.[22]
  • DesiRM: Designing of Complementary and Mismatch siRNAs for silencing a gene.[23]
  • GenomeABC: A server for benchmarking of genome assemblers.

References

  1. ^ "Computational Resources for Drug Discovery". Computational Resources for Drug Discovery homepage.
  2. PMID 19589147
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  7. ^ Nucleic Acids Research, 2011
  8. ^ "Open Source Drug Discovery". www.osdd.net. Retrieved 2023-09-08.
  9. PMID 23486013
    .
  10. ^ Raghava, G.P.S. "MycoTB: A Software for managing Mycobacterium Tuberculosis". crdd.osdd.net. Retrieved 2024-03-04.
  11. PMID 20637097
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  15. ^ Rashid, M. and Raghava, G. P. S. (2010) A simple approach for predicting protein–protein interactions. Current Protein & Peptide Science (In Press).
  16. PMID 20021687
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Further reading