import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem import Descriptors
from rdkit.Chem import Draw
import selfies as sf
from rdkit.Chem import RDConfig
import os
import sys
sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
import sascorer
def get_largest_ring_size(mol):
cycle_list = mol.GetRingInfo().AtomRings()
if cycle_list:
cycle_length = max([len(j) for j in cycle_list])
else:
cycle_length = 0
return cycle_length
def plogp(smile):
if smile:
mol = Chem.MolFromSmiles(smile)
if mol:
log_p = Descriptors.MolLogP(mol)
sas_score = sascorer.calculateScore(mol)
largest_ring_size = get_largest_ring_size(mol)
cycle_score = max(largest_ring_size - 6, 0)
if log_p and sas_score and largest_ring_size:
p_logp = log_p - sas_score - cycle_score
return p_logp
else:
return -100
else:
return -100
else:
return -100
def sf_decode(selfies):
try:
decode = sf.decoder(selfies)
return decode
except sf.DecoderError:
return ''
def sim(input_smile, output_smile):
if input_smile and output_smile:
input_mol = Chem.MolFromSmiles(input_smile)
output_mol = Chem.MolFromSmiles(output_smile)
if input_mol and output_mol:
input_fp = AllChem.GetMorganFingerprint(input_mol, 2)
output_fp = AllChem.GetMorganFingerprint(output_mol, 2)
sim = DataStructs.TanimotoSimilarity(input_fp, output_fp)
return sim
else: return None
else: return None
def gen_process(gen_input):
tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large")
sf_input = tokenizer(gen_input, return_tensors="pt")
# beam search
molecules = model.generate(input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
max_length=15,
min_length=5,
num_return_sequences=4,
num_beams=5)
gen_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
smis = [sf.decoder(i) for i in gen_output]
mols = []
for smi in smis:
mol = Chem.MolFromSmiles(smi)
mols.append(mol)
gen_output_image = Draw.MolsToGridImage(
mols,
molsPerRow=4,
subImgSize=(200,200),
legends=['' for x in mols]
)
return "\n".join(gen_output), gen_output_image
def opt_process(opt_input):
tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large-opt")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large-opt")
input = opt_input
smis_input = sf.decoder(input)
mol_input = []
mol = Chem.MolFromSmiles(smis_input)
mol_input.append(mol)
opt_input_img = Draw.MolsToGridImage(
mol_input,
molsPerRow=4,
subImgSize=(200,200),
legends=['' for x in mol_input]
)
sf_input = tokenizer(input, return_tensors="pt")
molecules = model.generate(
input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
do_sample=True,
max_length=100,
min_length=5,
top_k=30,
top_p=1,
num_return_sequences=10
)
sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
sf_output = list(set(sf_output))
input_sm = sf_decode(input)
sm_output = [sf_decode(sf) for sf in sf_output]
input_plogp = plogp(input_sm)
plogp_improve = [plogp(i)-input_plogp for i in sm_output]
simm = [sim(i,input_sm) for i in sm_output]
candidate_selfies = {"candidates": sf_output, "improvement": plogp_improve, "sim": simm}
data = pd.DataFrame(candidate_selfies)
results = data[(data['improvement']> 0) & (data['sim']>0.4)]
opt_output = results["candidates"].tolist()
opt_output_imp = results["improvement"].tolist()
opt_output_imp = [str(i) for i in opt_output_imp]
opt_output_sim = results["sim"].tolist()
opt_output_sim = [str(i) for i in opt_output_sim]
smis = [sf.decoder(i) for i in opt_output]
mols = []
for smi in smis:
mol = Chem.MolFromSmiles(smi)
mols.append(mol)
opt_output_img = Draw.MolsToGridImage(
mols,
molsPerRow=4,
subImgSize=(200,200),
legends=['' for x in mols]
)
return opt_input_img, "\n".join(opt_output), "\n".join(opt_output_imp), "\n".join(opt_output_sim), opt_output_img
with gr.Blocks() as demo:
gr.Markdown("# MolGen: Domain-Agnostic Molecular Generation with Self-feedback")
with gr.Tabs():
with gr.TabItem("Molecular Generation"):
with gr.Row():
with gr.Column():
gen_input = gr.Textbox(label="Input", lines=1, placeholder="SELFIES Input")
gen_button = gr.Button("Generate")
with gr.Column():
gen_output = gr.Textbox(label="Generation Results", lines=5, placeholder="")
gen_output_image = gr.Image(label="Visualization")
gr.Examples(
examples=[["[C][=C][C][=C][C][=C][Ring1][=Branch1]"],
["[C]"]
],
inputs=[gen_input],
outputs=[gen_output, gen_output_image],
fn=gen_process,
cache_examples=True,
)
with gr.TabItem("Constrained Molecular Property Optimization"):
with gr.Row():
with gr.Column():
opt_input = gr.Textbox(label="Input", lines=1, placeholder="SELFIES Input")
opt_button = gr.Button("Optimize")
with gr.Column():
opt_input_img = gr.Image(label="Input Visualization")
opt_output = gr.Textbox(label="Optimization Results", lines=3, placeholder="")
opt_output_imp = gr.Textbox(label="Optimization Property Improvements", lines=3, placeholder="")
opt_output_sim = gr.Textbox(label="Similarity", lines=3, placeholder="")
opt_output_img = gr.Image(label="Output Visualization")
gr.Examples(
examples=[["[C][C][=Branch1][C][=O][N][C][C][O][C][C][O][C][C][O][C][C][Ring1][N]"],
["[C][C][S][C][C][S][C][C][C][S][C][C][S][C][Ring1][=C]"],
["[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]"]
],
inputs=[opt_input],
outputs=[opt_input_img, opt_output, opt_output_imp, opt_output_sim, opt_output_img],
fn=opt_process,
cache_examples=True,
)
gen_button.click(fn=gen_process, inputs=[gen_input], outputs=[gen_output, gen_output_image])
opt_button.click(fn=opt_process, inputs=[opt_input], outputs=[opt_input_img, opt_output, opt_output_imp, opt_output_sim, opt_output_img])
demo.launch()