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import torch
import random
import pandas as pd
from utils import create_vocab, setup_seed
from dataset_mlm import get_paded_token_idx_gen, add_tokens_to_vocab
import gradio as gr
from gradio_rangeslider import RangeSlider
import time
is_stopped = False
seed = random.randint(0,100000)
setup_seed(seed)
def temperature_sampling(logits, temperature):
logits = logits / temperature
probabilities = torch.softmax(logits, dim=-1)
sampled_token = torch.multinomial(probabilities, 1)
return sampled_token
def stop_generation():
global is_stopped
is_stopped = True
return "Generation stopped."
def CTXGen(X0, X1, X2, τ, g_num, model_name):
device = torch.device("cpu")
vocab_mlm = create_vocab()
vocab_mlm = add_tokens_to_vocab(vocab_mlm)
save_path = model_name
train_seqs = pd.read_csv('C0_seq.csv')
train_seq = train_seqs['Seq'].tolist()
model = torch.load(save_path, map_location=torch.device('cpu'))
model = model.to(device)
global is_stopped
is_stopped = False
X3 = "X" * len(X0)
msa_data = pd.read_csv('conoData_C0.csv')
msa = msa_data['Sequences'].tolist()
msa = [x for x in msa if x.startswith(f"{X1}|{X2}")]
msa = random.choice(msa)
X4 = msa.split("|")[3]
X5 = msa.split("|")[4]
X6 = msa.split("|")[5]
model.eval()
with torch.no_grad():
new_seq = None
IDs = []
generated_seqs = []
generated_seqs_FINAL = []
cls_probability_all = []
act_probability_all = []
count = 0
gen_num = g_num
NON_AA = ["B","O","U","Z","X",'<K16>', '<α1β1γδ>', '<Ca22>', '<AChBP>', '<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<α4β2>',
'<GluN2B>', '<α75HT3>', '<Na14>', '<α7>', '<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>',
'<Ca12>', '<Na16>', '<α6α3β2>', '<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>',
'<Kshaker>', '<α3β4>', '<Na18>', '<α3β2>', '<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>','<α6β4>', '<α2β4>',
'<Na13>', '<Na12>', '<Na15>', '<α4β4>', '<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>',
'<α9α10>','<α6α3β4>', '<NaTTXS>', '<Na17>','<high>','<low>','[UNK]','[SEP]','[PAD]','[CLS]','[MASK]']
seq_parent = [f"{X1}|{X2}|{X0}|{X4}|{X5}|{X6}"]
padded_seqseq_parent, _, idx_msaseq_parent, _ = get_paded_token_idx_gen(vocab_mlm, seq_parent, new_seq)
idx_msaseq_parent = torch.tensor(idx_msaseq_parent).unsqueeze(0).to(device)
seqseq_parent = ["[MASK]" if i=="X" else i for i in padded_seqseq_parent]
seqseq_parent[1] = "[MASK]"
input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
cls_mask_logits_parent = logits_parent[0, 1, :]
cls_probability_parent, cls_mask_probs_parent = torch.topk((torch.softmax(cls_mask_logits_parent, dim=-1)), k=10)
seqseq_parent[2] = "[MASK]"
input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
act_mask_logits_parent = logits_parent[0, 2, :]
act_probability_parent, act_mask_probs_parent = torch.topk((torch.softmax(act_mask_logits_parent, dim=-1)), k=2)
cls_pos_parent = vocab_mlm.to_tokens(list(cls_mask_probs_parent))
act_pos_parent = vocab_mlm.to_tokens(list(act_mask_probs_parent))
cls_proba_parent = cls_probability_parent[cls_pos_parent.index(X1)].item()
act_proba_parent = act_probability_parent[act_pos_parent.index(X2)].item()
start_time = time.time()
while count < gen_num:
if is_stopped:
return pd.DataFrame(), "output.csv"
if time.time() - start_time > 1200:
break
gen_len = len(X0)
seq = [f"{X1}|{X2}|{X3}|{X4}|{X5}|{X6}"]
vocab_mlm.token_to_idx["X"] = 4
padded_seq, _, _, _ = get_paded_token_idx_gen(vocab_mlm, seq, new_seq)
input_text = ["[MASK]" if i=="X" else i for i in padded_seq]
gen_length = len(input_text)
length = gen_length - sum(1 for x in input_text if x != '[MASK]')
for i in range(length):
if is_stopped:
return pd.DataFrame(), "output.csv"
_, idx_seq, idx_msa, attn_idx = get_paded_token_idx_gen(vocab_mlm, seq, new_seq)
idx_seq = torch.tensor(idx_seq).unsqueeze(0).to(device)
idx_msa = torch.tensor(idx_msa).unsqueeze(0).to(device)
attn_idx = torch.tensor(attn_idx).to(device)
mask_positions = [j for j in range(gen_length) if input_text[j] == "[MASK]"]
mask_position = torch.tensor([mask_positions[torch.randint(len(mask_positions), (1,))]])
logits = model(idx_seq,idx_msa, attn_idx)
mask_logits = logits[0, mask_position.item(), :]
predicted_token_id = temperature_sampling(mask_logits, τ)
predicted_token = vocab_mlm.to_tokens(int(predicted_token_id))
input_text[mask_position.item()] = predicted_token
padded_seq[mask_position.item()] = predicted_token.strip()
new_seq = padded_seq
generated_seq = input_text
generated_seq[1] = "[MASK]"
input_ids = vocab_mlm.__getitem__(generated_seq)
logits = model(torch.tensor([input_ids]).to(device), idx_msa)
cls_mask_logits = logits[0, 1, :]
cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=10)
generated_seq[2] = "[MASK]"
input_ids = vocab_mlm.__getitem__(generated_seq)
logits = model(torch.tensor([input_ids]).to(device), idx_msa)
act_mask_logits = logits[0, 2, :]
act_probability, act_mask_probs = torch.topk((torch.softmax(act_mask_logits, dim=-1)), k=2)
cls_pos = vocab_mlm.to_tokens(list(cls_mask_probs))
act_pos = vocab_mlm.to_tokens(list(act_mask_probs))
if X1 in cls_pos and X2 in act_pos:
cls_proba = cls_probability[cls_pos.index(X1)].item()
act_proba = act_probability[act_pos.index(X2)].item()
generated_seq = generated_seq[generated_seq.index('[MASK]') + 2:generated_seq.index('[SEP]')]
if cls_proba>=cls_proba_parent and act_proba>=act_proba_parent and generated_seq.count('C') % 2 == 0 and len("".join(generated_seq)) == gen_len:
generated_seqs.append("".join(generated_seq))
if "".join(generated_seq) not in train_seq and "".join(generated_seq) not in generated_seqs[0:-1] and all(x not in NON_AA for x in generated_seq):
generated_seqs_FINAL.append("".join(generated_seq))
cls_probability_all.append(cls_proba)
act_probability_all.append(act_proba)
IDs.append(count+1)
out = pd.DataFrame({
'ID':IDs,
'Generated_seq': generated_seqs_FINAL,
'Subtype': X1,
'Subtype_probability': cls_probability_all,
'Potency': X2,
'Potency_probability': act_probability_all,
'Random_seed': seed
})
out.to_csv("output.csv", index=False, encoding='utf-8-sig')
count += 1
yield out, "output.csv"
return out, "output.csv"
with gr.Blocks() as demo:
gr.Markdown("# Conotoxin Optimization Generation")
with gr.Row():
X0 = gr.Textbox(label="conotoxin")
X1 = gr.Dropdown(choices=['<α7>','<AChBP>','<α4β2>','<α3β4>','<Ca22>','<α3β2>', '<Na12>','<α9α10>','<K16>', '<α1β1γδ>',
'<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<GluN2B>', '<α75HT3>', '<Na14>',
'<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>', '<Ca12>', '<Na16>', '<α6α3β2>',
'<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>', '<Kshaker>', '<Na18>',
'<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>','<α6β4>', '<α2β4>','<Na13>', '<Na15>', '<α4β4>',
'<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>', '<α6α3β4>', '<NaTTXS>', '<Na17>'], label="Subtype")
X2 = gr.Dropdown(choices=['<high>','<low>'], label="Potency")
model_name = gr.Dropdown(choices=['model_final','model_C1','model_C2','model_C3','model_C4','model_C5','model_mlm'], label="Model")
τ = gr.Slider(minimum=1, maximum=2, step=0.1, label="τ")
g_num = gr.Dropdown(choices=[1, 10, 20, 30, 40, 50], label="Number of generations")
with gr.Row():
start_button = gr.Button("Start Generation")
stop_button = gr.Button("Stop Generation")
with gr.Row():
output_df = gr.DataFrame(label="Generated Conotoxins")
with gr.Row():
output_file = gr.File(label="Download generated conotoxins")
start_button.click(CTXGen, inputs=[X0, X1, X2, τ, g_num, model_name], outputs=[output_df, output_file])
stop_button.click(stop_generation, outputs=None)
demo.launch() |