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md5=d12b87ec8a1a361ef4d9fa1c23615869
README.md.html
FASMIFRA
Reference implementation for the article
"Fast Molecular Generation by Assembly of (Deep)SMILES Fragments".
Generate molecules fast from a molecular training set while also
doing training-set distribution matching.

Installing the software
On Linux, you need python3-rdkit to be installed.
For OCaml programmers, you can clone this repository
then type 'make && make install'.
Note that you need to have opam installed and configured.
For end-users:
sudo apt install opam
opam init
eval `opam config env`
opam install fasmifra
For programmer end-users, or if the opam package is not ready yet:
sudo apt install opam
opam init
eval `opam config env`
opam pin add fasmifra https://github.com/UnixJunkie/FASMIFRA.git
We are currently working on an automated self-installer; stay tuned.
Fragmenting molecules
Those molecules are your "molecular training set".
fasmifra_fragment.py -i my_molecules.smi -o my_molecules_frags.smi
If you fragment rather small molecules, you might want to use the -w option
and pass a smaller recommended fragment weight than the default (150 Da).
usage: fasmifra_fragment.py [-h] [-i input.smi] [-o output.smi] [--seed SEED]
[-n NB_PASSES] [-w FRAG_WEIGHT]
fragment molecules (tag cleaved bonds)
optional arguments:
-h, --help show this help message and exit
-i input.smi molecules input file
-o output.smi fragments output file
--seed SEED RNG seed
-n NB_PASSES number of fragmentation passes
-w FRAG_WEIGHT fragment weight (default=150Da)
Generating molecules from fragments
fasmifra -n 100000 -i my_molecules_frags.smi -o my_molecules_gen.smi
usage:
fasmifra
-n <int>: how many molecules to generate
-i <filename>: smiles fragments input file
-o <filenams>: output file
[--seed <int>]: RNG seed
[--deep-smiles]: input/output molecules in DeepSMILES no-rings format
Bibliography
[1] Berenger, F., & Tsuda, K. (2021).
"Fast Molecular Generation by Assembly of (Deep)SMILES Fragments".
Submitted manuscript.
[2] O'Boyle, N., & Dalke, A. (2018).
"DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures".
chemrxiv.org
[3] Weininger, D. (1988). SMILES, a chemical language and information system.
"1. Introduction to methodology and encoding rules".
Journal of chemical information and computer sciences, 28(1), 31-36.