QSAR Study of HIV Protease Inhibitors Using Neural Network and Genetic Algorithm

1
QSAR Study of HIV Protease Inhibitors Using
Neural Network and Genetic Algorithm
Akmal Aulia,1 Sunil Kumar,2 Rajni Garg, 3 A.
Srinivas Reddy,4
Descriptor Thinning
Results
Introduction
IC50 dataset
Total Descriptors

• Linear and Non-linear regression techniques are
  employed to analyze a large dataset of 334
  compounds of HIV protease inhibitors (Kempf et
  al.).
• The data set was studied using MLR (Multiple
  Linear Regression) and ANN (Artificial Neural
  Network) techniques to develop QSAR (Quantitative
  Structure-Activity Relationship) models.
• Each ligand (inhibitor or drug molecule) was
  described by means of physico-chemical and
  structural descriptors (features) which encode
  constitutional, electrostatic, geometrical,
  quantum and topological properties.
• The capability of descriptors to address the
  variations in ligand(s) was linked to the
  predictive power of QSAR models.
• Combined information from these models helps in
  'transforming data into information and
  information into knowledge' from chem-informatics
  point of view.

IC50 set Final Descriptors
EC50 dataset
Materials and Methods
EC50 set Final Descriptors
Research Design



Reported dataset (Kempf et al.)
with their experimental Biological Activity (EC50
and IC50)?



Lower energy conformation is obtained
for each compound by means of Molecular Mechanics
Minimization.



A total of 277
descriptors calculated.
Summary Future Work



Objective Descriptors(Matlab)
IC50 dataset(reduced from 277 to 148), EC50
dataset(reduced from 277 to 157). Subjective
Descriptors(WEKA/GA) IC50 dataset(reduced from
148 to 9), EC50 dataset(reduced from 157 to 7)?

• For the IC50 dataset, the constitutional and
  topological properties have the largest
  contribution, while for the EC50 dataset,
  electrostatic and topological properties are
  significant.
• Non-linear models have better predictive
  capability. However, the linear models can be
  interpreted better mechanistically. Presence of
  similar descriptors in both types of models
  validates our results.
• Further studies using other statistical and ANN
  based regression techniques are in progress, in
  order to find the best QSAR models and
  descriptors.
• These models will serve as useful computational
  tools for prediction of biological activity of
  this class of HIV protease inhibitors.

Both MLR and FNN methods were implemented in
WEKA.
References
(1) Fernandez et al. Quantitative
structure-activity relationship to predict
differential inhibition of aldose reductase by
flavonoid compounds Bioorganic and Medicinal
Chemistry, 2005, 13, 3269-3277. (2) (a)CODESSA
software, Semichem Inc., USA (b) MATLAB, The
MathWorks Inc. (c) WEKA software, the University
of Waikato, New Zealand. (3) Fernandez, M. and
Caballero, J.Linear and nonlinear modeling of
antifungal activity of some heterocyclic ring
derivatives using multiple linear regression and
Bayesian-regularized neural networks, J. Mol.
Model., 2006, 12, 168-181 (4) Goldberg, D. E.
Genetic Algorithms in Search Optimization
Machine Learning Addison-WesleyReading, MA,
2000. (5) Data Mining Practical Machine
Learning tools and techniques, 2nd Edition,
Morgan Kaufmann, San Fransisco, 2005.
1Computational Science Research Center, San Diego
State University, CA 2ECE Dept., San Diego State
University, San Diego, CA 3Chem. Dept.,
California State University, San Marcos, CA
4Molecular Modeling Group, IICT, Hyderabad, India.