Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian semi-supervised learning for uncertainty-calibrated prediction
  of molecular properties and active learning   Predicting bioactivity and physical properties of small molecules is a
central challenge in drug discovery. Deep learning is becoming the method of
choice but studies to date focus on mean accuracy as the main metric. However,
to replace costly and mission-critical experiments by models, a high mean
accuracy is not enough: Outliers can derail a discovery campaign, thus models
need reliably predict when it will fail, even when the training data is biased;
experiments are expensive, thus models need to be data-efficient and suggest
informative training sets using active learning. We show that uncertainty
quantification and active learning can be achieved by Bayesian semi-supervised
graph convolutional neural networks. The Bayesian approach estimates
uncertainty in a statistically principled way through sampling from the
posterior distribution. Semi-supervised learning disentangles representation
learning and regression, keeping uncertainty estimates accurate in the low data
limit and allowing the model to start active learning from a small initial pool
of training data. Our study highlights the promise of Bayesian deep learning
for chemistry.

7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci