Comments (4)
Got it!
from molgen.
Hi,
- Yes, all the experiments in the paper require the input of molecules, as the base model is the BART model.
- 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.
- 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]
- 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.
Related Issues (13)
- About the molecule generation with optimized chemical properties HOT 3
- About the Vocabulary HOT 3
- Finetuning HOT 2
- Question about "loss_output" of "finetune.py" HOT 9
- About loading the checkpoint file for generative task HOT 1
- 为什么说 MPT 可以在不同 tasks 和 domains 之间共享知识呢? HOT 5
- Question about ”generation of two-dimensional molecules“ HOT 2
- Reduntant import HOT 1
- Pretrain and finetune datasets HOT 2
- Details on Mol-Gen 7b HOT 2
- How to reproduce the impressive scores shown in Table 1? 如何复现Table 1的优异结果? HOT 5
- About preprocess HOT 2
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from molgen.