Classification of oral bioavailability of drugs by machine learning approaches: a comparative study

Oral Bioavailability is the rate and extent to which an active drug substance is absorbed and becomes available to the general circulation. A computational model for the prediction of oral bioavailability is a vital initial step in the drug discovery. It is decisive for selecting the promising compounds for the next level optimizations and recognition for the clinical trials. In the present investigation we aimed to perform the oral bioavailability prediction by comparing three machine learning methods i.e. Support Vector Machine (SVM) based kernel learning, Artificial Neural Network (ANN) and Bayesian classification approach. The overall prediction efficiency of SVM based model for the test set was 96.85%, whereas according to the Bayesian classifier and ANN methods prediction efficiency was found to be 92.19% and 94.53% respectively. Thus the present results clearly suggested that the SVM based prediction of oral bioavailability of drugs is more efficient binary classification approach for the data under consideration.

[1]  Joseph V. Turner,et al.  Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs , 2004, Pharmaceutical Research.

[2]  Louis S. Goodman,et al.  The Pharmacological Basis of Therapeutics. , 1941 .

[3]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[4]  Marjo Yliperttula,et al.  Computational prediction of oral drug absorption based on absorption rate constants in humans. , 2006, Journal of medicinal chemistry.

[5]  Leonardo Vanneschi,et al.  Genetic programming for human oral bioavailability of drugs , 2006, GECCO.

[6]  M. Kuentz,et al.  Influence of molecular properties on oral bioavailability of lipophilic drugs – Mapping of bulkiness and different measures of polarity , 2009, Pharmaceutical development and technology.

[7]  H. X. Liu,et al.  The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine , 2005, J. Comput. Aided Mol. Des..

[8]  Gilles Klopman,et al.  ADME evaluation. 2. A computer model for the prediction of intestinal absorption in humans. , 2002, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[9]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[10]  J. Topliss,et al.  QSAR model for drug human oral bioavailability. , 2000, Journal of medicinal chemistry.

[11]  I. Muegge,et al.  Computational methods to estimate drug development parameters. , 2001, Current opinion in drug discovery & development.

[12]  Chandrika Kamath,et al.  Feature selection in scientific applications , 2004, KDD.

[13]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[14]  Anne Hersey,et al.  Rate-Limited Steps of Human Oral Absorption and QSAR Studies , 2002, Pharmaceutical Research.

[15]  Junmei Wang,et al.  Genetic Algorithm-Optimized QSPR Models for Bioavailability, Protein Binding, and Urinary Excretion , 2006, J. Chem. Inf. Model..

[16]  John G. Topliss,et al.  QSAR Model for Drug Human Oral Bioavailability1 , 2000 .

[17]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[18]  Lemont B. Kier,et al.  Modeling Drug Albumin Binding Affinity with E-State Topological Structure Representation , 2003, J. Chem. Inf. Comput. Sci..

[19]  Tingjun Hou,et al.  ADME evaluation in drug discovery , 2002, Journal of molecular modeling.

[20]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[21]  Antonio Chana,et al.  CODES/neural network model: A useful tool for in silico prediction of oral absorption and blood-brain barrier permeability of structurally diverse drugs , 2004 .

[22]  P. Sinko,et al.  Estimating Human Drug Oral Absorption Kinetics from Caco-2 Permeability Using an Absorption-Disposition Model: Model Development and Evaluation and Derivation of Analytical Solutions for ka and Fa , 2005, Journal of Pharmacology and Experimental Therapeutics.

[23]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules? , 2007, J. Chem. Inf. Model..

[24]  M. Eichelbaum,et al.  Prediction of bioavailability for drugs with a high first-pass effect using oral clearance data , 2004, European Journal of Clinical Pharmacology.

[25]  Tudor I. Oprea,et al.  Toward minimalistic modeling of oral drug absorption. , 1999, Journal of molecular graphics & modelling.

[26]  Xin Chen,et al.  Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents , 2004, J. Chem. Inf. Model..