Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AI challenges for predicting the impact of mutations on protein
  stability   Stability is a key ingredient of protein fitness and its modification through
targeted mutations has applications in various fields such as protein
engineering, drug design and deleterious variant interpretation. Many studies
have been devoted over the past decades to building new, more effective methods
for predicting the impact of mutations on protein stability, based on the
latest developments in artificial intelligence (AI). We discuss their features,
algorithms, computational efficiency, and accuracy estimated on an independent
test set. We focus on a critical analysis of their limitations, the recurrent
biases towards the training set, their generalizability and interpretability.
We found that the accuracy of the predictors has stagnated at around 1 kcal/mol
for over 15 years. We conclude by discussing the challenges that need to be
addressed to reach improved performance.

29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th