Impact and Challenges of Chemoinformatics in Drug Discovery

Pochi R. Subbarayan

Research output: Chapter in Book/Report/Conference proceedingChapter


The last decade has witnessed the integration of systems biology and Chemoinformatics datasets. Many open source and commercial programs have streamlined drug discovery research efforts. In this chapter, we report the predictive accuracy of PASS and MetaDrug, two commercial Chemoinformatics programs, in deciphering biological activity of the test compound PE3. Both programs identified PE3 as an antineoplastic and antiviral agent. PASS recognized PE3 as a photosensitizer with a high degree of confidence, while MetaDrug failed to determine this important property. Overall, PASS predicted the biological properties of PE3 better than MetaDrug. At present, no single Chemoinformatics program can conclusively predict the biological activity spectrum of new chemical entities (NCEs). Therefore, we recommend using several Chemoinformatics programs and experimental data to predict drug-like properties of NCEs. Such an approach may accelerate drug discovery.

Original languageEnglish (US)
Title of host publicationArtificial Neural Network for Drug Design, Delivery and Disposition
PublisherElsevier Inc.
Number of pages12
ISBN (Print)9780128015599
StatePublished - 2016


  • Natural products
  • Shotgun approach to drug discovery
  • Systematic drug discovery efforts
  • Systems biology
  • Target deconvolution
  • Virtual screening

ASJC Scopus subject areas

  • Medicine(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)


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