Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning with Real-time Docking of 3D Structures to Cover
  Chemical Space: Mining for Potent SARS-CoV-2 Main Protease Inhibitors   We propose a novel framework that generates new inhibitor molecules for
target proteins by combining deep reinforcement learning (RL) with real-time
molecular docking on 3-dimensional structures. We illustrate the inhibitor
mining (iMiner) approach on the main protease (MPro) protein of SARS-COV-2 that
is further validated via consensus of two different docking software, as well
as for druglikeness and ease of synthesis. Ultimately 54 molecules are proposed
as potent Mpro inhibitors (7 of which have better synthetic accessibility),
covering a much broader range than crowd-sourced projects like the COVID
moonshot, and our generated molecules exhibit an optimized, precise, and
energetically consistent fit within the catalytic binding pocket. Moreover, our
approach only relies on the structure of the target protein, which means it can
be easily adapted for future development of other inhibitors of any protein of
disease origin.

15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas