Pharmaceutical Research

, Volume 24, Issue 12, pp 2249–2262 | Cite as

Computational Models to Assign Biopharmaceutics Drug Disposition Classification from Molecular Structure

  • Akash Khandelwal
  • Praveen M. Bahadduri
  • Cheng Chang
  • James E. Polli
  • Peter W. Swaan
  • Sean Ekins
Research Paper



We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS).

Materials and Methods

Models were developed using machine learning methods including recursive partitioning (RP), random forest (RF) and support vector machine (SVM) algorithms with ChemDraw, clogP, polar surface area, VolSurf and MolConnZ descriptors. The dataset consisted of 165 training and 56 test set molecules.


RF model 3, RP model 1, and SVM model 1 can correctly predict 73.1, 63.6 and 78.6% test compounds in classes 1, 2 and 3, respectively. Both RP and SVM models can be used for class 4 prediction. The inclusion of consensus analysis resulted in improved test set predictions for class 2 and 4 drugs.


The models can be used to predict BDDCS class for new compounds from molecular structure using readily available molecular descriptors and software, representing an area where in silico approaches could aid the pharmaceutical industry in speeding drugs to the patient and reducing costs. This could have significant applications in drug discovery to identify molecules that may have future developability issues.

Key words

BCS BDDCS random forest recursive partitioning support vector machine 



Biopharmaceutics Classification System


Biopharmaceutics Drug Disposition Classification System


random forest


recursive partitioning


support vector machine



Akash Khandelwal and Praveen M. Bahadduri contributed equally to this work and should be considered co-first authors.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Akash Khandelwal
    • 1
  • Praveen M. Bahadduri
    • 1
  • Cheng Chang
    • 1
    • 3
  • James E. Polli
    • 1
  • Peter W. Swaan
    • 1
  • Sean Ekins
    • 1
    • 2
  1. 1.Department of Pharmaceutical SciencesUniversity of MarylandBaltimoreUSA
  2. 2.ACT LLCJenkintownUSA
  3. 3.Current address: Pfizer, IncGrotonUSA

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