Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Througput pKa Prediction Using Semi Empirical Methods   A large proportion of organic molecules relevant to medicine and
biotechnology contain one or more ionizable groups, which means that
fundamental physical and chemical properties (e.g. the charge of the molecule)
depend on the pH of the surroundings via the corresponding pKa values of the
molecules. As drug- and material design increasingly is being done through high
throughput screens, fast - yet accurate - computational pKa prediction methods
are becoming crucial to the design process. Current empirical pKa predictors
are increasingly found to fail because they are being applied to parts of
chemical space for which experimental parameterization data is lacking. We
propose to develop a pKa predictor that, due its quantum mechanical foundation,
is more generally applicable but still fast enough to be used in high
throughput screening. The method has the potential to impact virtually any
biotechnological design process involving organic molecules as we will
demonstrate for metabolic engineering and organic battery design.

23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con