NDLI: Computational Models to Assign Biopharmaceutics Drug Disposition Classification from Molecular Structure

Content Provider	Springer Nature Link
Author	Khandelwal, Akash Bahadduri, Praveen M. Chang, Cheng Polli, James E. Swaan, Peter W. Ekins, Sean
Copyright Year	2007
Abstract	We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS).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.
Starting Page	2249
Ending Page	2262
Page Count	14
File Format	PDF
ISSN	07248741
Journal	Pharmaceutical Research
Volume Number	24
Issue Number	12
e-ISSN	1573904X
Language	English
Publisher	Springer US
Publisher Date	2007-09-11
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	BCS BDDCS random forest recursive partitioning support vector machine Biomedical Engineering Medical Law Biochemistry Pharmacy Pharmacology/Toxicology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Pharmacology Molecular Medicine Pharmacology (medical) Biotechnology Pharmaceutical Science

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