About the generation process about molgen HOT 4 CLOSED

Got it!

from molgen.

Hi,

1. Yes, all the experiments in the paper require the input of molecules, as the base model is the BART model.
2. Your understanding is completely correct; this is a work on molecular optimization.

from molgen.

Thank you!
3、And how about MolGen 7B generate molecules? Is there any prompt gives to the trained model to start the generation process?
4、Does MolGen 7B designed for the 'generation from scratch' mission (generation and estimate the whole distribution, compare the distribution with the trainingset) or it can also complete the optimization mission?
Again thanks very much!

from molgen.

3. MolGen 7B is capable of generating molecules from scratch. You can input a bos_token, or input an incomplete structure for the model to complete.

Denovo molecule generation example:

from transformers import AutoTokenizer, LlamaForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-7b")
model = LlamaForCausalLM.from_pretrained(
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sf_input = tokenizer(tokenizer.bos_token, return_tensors="pt").to(device)

molecules = model.generate(input_ids=sf_input["input_ids"],
sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]

Molecular completion example:

from transformers import AutoTokenizer, LlamaForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-7b")
model = LlamaForCausalLM.from_pretrained(
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sf_input = tokenizer("[C][N][O]", return_tensors="pt").to(device)

molecules = model.generate(input_ids=sf_input["input_ids"],
sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]

4. MolGen 7B is primarily designed for tasks involving the de novo generation of molecules or the completion of molecular structures. However, by making appropriate modifications to the model's generate function, it can also support inputting molecular embeddings and can be used for optimization tasks as well.

from molgen.