Computational Intelligence - August 2015 - 19
Weighted Tanimoto
Extreme Learning Machine with
Case Study in Drug Discovery
Wojciech Marian Czarnecki
Faculty of Mathematics and Computer Science,
Jagiellonian University, Krakow, POLAND
©CRE
ATAS
Abstract-Machine
-Machine learning methods are becoming more and
more popular in the field of computer-aided drug design.
The specific data characteristic, including sparse, binary representation as well as noisy, imbalanced datasets, presents a
challenging binary classification problem. Currently, two of
the most successful models in such tasks are the Support
Vector Machine (SVM) and Random Forest (RF). In this
paper, we introduce a Weighted Tanimoto Extreme
Learning Machine (T-WELM), an extremely simple and
fast method for predicting chemical compound biological activity and possibly other data with discrete, binary
representation. We show some theoretical properties of
the proposed model including the ability to learn arbitrary sets of examples. Further analysis shows numerous advantages of T-WELM over SVMs, RFs and
traditional Extreme Learning Machines (ELM) in this
particular task. Experiments performed on 40 large
datasets of thousands of chemical compounds show
that T-WELMs achieve much better classification
results and are at the same time faster in terms of
both training time and further classification than
both ELM models and other state-of-the-art
methods in the field.
C
I. Introduction
omputer-aided drug design has become a very popular technique for speeding up the process of finding
new biologically active compounds. Despite the existence of huge databases of known drugs as well as
numerous approaches to predicting the activity of different
molecules, this problem is still very challenging and even good
generation of training sets [1], data representation [2] and evaluation metrics [3] are open problems. One of the recognized
bottlenecks in this field is the size of the datasets required to
Digital Object Identifier 10.1109/MCI.2015.2437312
Date of publication: 16 July 2015
1556-603X/15©2015IEEE
process. There are hundreds of thousands of compounds in
databases and numerous proteins for which we would like to
know the activity. Furthermore, the resulting datasets are very
noisy and highly imbalanced.
The main aim of this paper is to introduce a new machine
learning model, the Tanimoto Extreme Learning Machine
(T-ELM), which is well suited for this type of problem, has a
strong theoretical background, achieves good classification
results, and is extremely fast (in terms of both training and
evaluation times). T-ELM is a modification of the Extreme
AUGUST 2015 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
19
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