Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning   The efficacy of a drug depends on its binding affinity to the therapeutic
target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable
progress in predicting drug efficacy. We develop MolDesigner, a
human-in-the-loop web user-interface (UI), to assist drug developers leverage
DL predictions to design more effective drugs. A developer can draw a drug
molecule in the interface. In the backend, more than 17 state-of-the-art DL
models generate predictions on important indices that are crucial for a drug's
efficacy. Based on these predictions, drug developers can edit the drug
molecule and reiterate until satisfaction. MolDesigner can make predictions in
real-time with a latency of less than a second.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
3
The categories of this paper are: physics