Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Focussed Molecule Libraries for Drug Discovery with Recurrent
  Neural Networks   In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language processing. We demonstrate that the properties of the generated
molecules correlate very well with the properties of the molecules used to
train the model. In order to enrich libraries with molecules active towards a
given biological target, we propose to fine-tune the model with small sets of
molecules, which are known to be active against that target.
  Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
molecules that medicinal chemists designed, whereas against Plasmodium
falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
with a scoring function, our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom