Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Development and evaluation of a deep learning model for protein-ligand
  binding affinity prediction   Structure based ligand discovery is one of the most successful approaches for
augmenting the drug discovery process. Currently, there is a notable shift
towards machine learning (ML) methodologies to aid such procedures. Deep
learning has recently gained considerable attention as it allows the model to
"learn" to extract features that are relevant for the task at hand. We have
developed a novel deep neural network estimating the binding affinity of
ligand-receptor complexes. The complex is represented with a 3D grid, and the
model utilizes a 3D convolution to produce a feature map of this
representation, treating the atoms of both proteins and ligands in the same
manner. Our network was tested on the CASF "scoring power" benchmark and Astex
Diverse Set and outperformed classical scoring functions. The model, together
with usage instructions and examples, is available as a git repository at
http://gitlab.com/cheminfIBB/pafnucy

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex