Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Toxicity Prediction via Simple Features Using Shallow Neural
  Networks and Decision Trees   Toxicity prediction of chemical compounds is a grand challenge. Lately, it
achieved significant progress in accuracy but using a huge set of features,
implementing a complex blackbox technique such as a deep neural network, and
exploiting enormous computational resources. In this paper, we strongly argue
for the models and methods that are simple in machine learning characteristics,
efficient in computing resource usage, and powerful to achieve very high
accuracy levels. To demonstrate this, we develop a single task-based chemical
toxicity prediction framework using only 2D features that are less compute
intensive. We effectively use a decision tree to obtain an optimum number of
features from a collection of thousands of them. We use a shallow neural
network and jointly optimize it with decision tree taking both network
parameters and input features into account. Our model needs only a minute on a
single CPU for its training while existing methods using deep neural networks
need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better
performance on several toxicity benchmark tasks. We also develop a cumulative
feature ranking method which enables us to identify features that can help
chemists perform prescreening of toxic compounds effectively.

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: econ
27
The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th