Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A decision theoretic approach to model evaluation in computational drug
  discovery   Artificial intelligence, trained via machine learning or computational
statistics algorithms, holds much promise for the improvement of small molecule
drug discovery. However, structure-activity data are high dimensional with low
signal-to-noise ratios and proper validation of predictive methods is
difficult. It is poorly understood which, if any, of the currently available
machine learning algorithms will best predict new candidate drugs. 25 publicly
available molecular datasets were extracted from ChEMBL. Neural nets, random
forests, support vector machines (regression) and ridge regression were then
fitted to the structure-activity data. A new validation method, based on
quantile splits on the activity distribution function, is proposed for the
construction of training and testing sets. Model validation based on random
partitioning of available data favours models which overfit and `memorize' the
training set, namely random forests and deep neural nets. Partitioning based on
quantiles of the activity distribution correctly penalizes models which can
extrapolate onto structurally different molecules outside of the training data.
This approach favours more constrained models, namely ridge regression and
support vector regression. In addition, our new rank-based loss functions give
considerably different results from mean squared error highlighting the
necessity to define model optimality with respect to the decision task at hand.
Model performance should be evaluated from a decision theoretic perspective
with subjective loss functions. Data-splitting based on the separation of high
and low activity data provides a robust methodology for determining the best
extrapolating model. Simpler, traditional statistical methods such as ridge
regression outperform state-of-the-art machine learning methods in this
setting.

35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph