Hello. This might be an oddly specific question.

Running the tutorial Getting Started with SAFE, the decoded example in version 0.1.3 for the safe_fragment[0] is:
'FC(F)(F)c1cc([:2])n([:3])n1'

However, this changes in version 0.1.4, where the safe_fragment[0] is now:
'FC(F)(F)c1cc([:4])n([:3])n1'

Is the new version now reading molecules differently in version 0.1.4?

Hi Emmanuel, it is Ulrich (I hola you on linkedIn).
Thanks for the amazing work. I wanted to find out if there is a way to identify the starting_number used for the new rings from the safe encoded string (https://github.com/datamol-io/safe/blob/main/safe/converter.py#L330). I want to experiment with a slightly different representation of safe but I want a neat mechanism to convert from safe to my representation and back.

I took this smile as an example, the one in the paper.
repr = encode("O=C(C#CCN1CCCCC1)Nc1ccc2ncnc(Nc3cccc(Br)c3)c2c1", canonical=False)
running the above I get 'O=C7C#CC6.N16CCCCC1.N74.c14ccc2ncnc8c2c1.N85.c15cccc(Br)c1'

The starting_number here is 4 but is there any of the functions I can use to know this.

• Revisit readme
• Clean Code
• build docs
• models availability
• training scripts.
• application demo

Hi, awesome stuff.

what does mc_labels contain? there's no info about it thank you

The CC-BY license is very explicit about permitting commercial use, contrary to the description in the README:

https://github.com/datamol-io/safe/blob/main/README.md?plain=1#L163

Can you clarify what the actual license is? Is it actually CC-BY, or did you intend to apply a non-commercial license?

Thanks.

Amazing work guys! Very impressed.

We had an issue with encode func on the following molecule:

import safe as sf
eh=sf.encode('CC1=CC=C(C=C1)C#CC1=NC=C(C=C1)C(F)(F)F')
print(eh)
#Cc1ccc(C#Cc2ccc2cn2)cc1.C2(F)(F)F
#expecting:
#Cc1ccc(C#Cc2ccc3cn2)cc1.C3(F)(F)F
sf.to_image('Cc1ccc(C#Cc2ccc3cn2)cc1.C3(F)(F)F')

Thank you!

Thanks for packaging up SAFE in such a nice package!

I've been trying

safe.encode(smiles, seed=42, canonical=True, randomize=False)

with smiles=CC(Cc1ccc(cc1)C(C(=O)O)C)C (using the latest PyPi release)

but receive sometimes c12ccc3cc1.C3(C)C(=O)O.CC(C)C2, othertimes c13ccc2cc1.C2(C)C(=O)O.CC(C)C3.

I realize that the representations are equivalent but wondered if the canonical output was not supposed to be determinstic?

Cheers,
Kevin

I noticed that if the slicer (in this case BRICS) breaks double bonds, the resulting fragment cannot be properly decoded.

Using such a molecule and following the documentation:

import safe as sf

safe_str = sf.encode("C(=C/c1ccccc1)\CCc1ccccc1", canonical=True)
print(safe_str)

safe_fragment = safe_str.split(".")
sf.decode(safe_fragment[0], as_mol=True)

I get: SAFEDecodeError: Failed to decode C(=2)c1ccccc1

I think the more appropriate output might be C(=[*])c1ccccc1??

When running the following script:

config_path="../trainer/configs/small_config.json"
tokenizer_path="../tokenizer.json"
dataset_path="../../Datasets/MOSES/datasets"
output_dir="./trained/SAFE_small"

safe-train --config $config_path \
  --tokenizer $tokenizer_path \
  --dataset $dataset_path \
  --text_column "SAFE" \
  --torch_compile True \
  --optim "adamw_torch" \
  --learning_rate 5e-4 \
  --prop_loss_coeff 1e-3 \
  --gradient_accumulation_steps 1 \
  --output_dir $output_dir \
  --num_labels 9 \
  --num_train_epochs 3 \
  --per_device_train_batch_size 8 \
  --lr_scheduler_type "linear" \
  --warmup_steps 500 \
  --logging_steps 100 \
  --evaluation_strategy "steps" \
  --eval_steps 500 \
  --save_steps 500 \
  --load_best_model_at_end True \
  --metric_for_best_model "eval_loss" \
  --greater_is_better False

I get the error:

Traceback (most recent call last):
File "/home/lmbanr001/.local/bin/safe-train", line 8, in
sys.exit(main())
File "/home/lmbanr001/.local/lib/python3.10/site-packages/safe/trainer/cli.py", line 406, in main
train(model_args, data_args, training_args)
File "/home/lmbanr001/.local/lib/python3.10/site-packages/safe/trainer/cli.py", line 335, in train
trainer = SAFETrainer(
File "/home/lmbanr001/.local/lib/python3.10/site-packages/safe/trainer/trainer_utils.py", line 19, in init
self.accelerator.dispatch_batches = dispatch_batches
AttributeError: can't set attribute 'dispatch_batches'

As I understand it, dispatch_batches is set to true when using another for of ingesting the data. Is there some intuition as to why my code is trying to set dispatch_batches?

Hi,

Firstly, thank you for making this excellent repository. The SAFE paper has some really interesting insights on molecular design.

As I was going over the codebase, I did not find any codes for the reinforcement learning part. Could you please let me know if I am missing something?
Also, how are you getting the advantage estimates in the ppo loss ? Is there an additional value network ?

Apologies if the questions are too obvious. I would really appreciate you insights on this.

Hi, I found a few artifacts when dealing with SMILES that contain the wildcard *, specially in rings. In the attached screenshot, you can see that a ring becomes a 'square' after encoding and decoding. This happens when converting from BRICS to SAFE.

I am using the decoder to decode individual fragments and I notice that if the fragment smiles contain square brackets then the following decoding fails:

import safe as sf
import datamol as dm

example_failed = """
O=C(CN1CC[NH2+]CC1)N1CCCCC1
[NH3+]Cc1ccccc1
c1cc2c(cc1[C@@H]1CCC[NH2+]1)OCCO2
"""


safe_obj = sf.SAFEConverter(slicer="brics", require_hs=False)

safe_str = sf.encode("c1cc2c(cc1[C@@H]1CCC[NH2+]1)OCCO2", canonical=True)
 safe_fragment = safe_str.split(".")

  with dm.without_rdkit_log():
      for frag in safe_fragment:
            f = safe_obj.decoder(
                frag,
                as_mol=False,
                canonical=False, 
                fix=True,
                remove_dummies=True,
                remove_added_hs=True,
           )
           if f is None: print(frag)

This returns None and I feel this is due to the way square brackets are parsed in the decoder.

Thank you.

Hi Emmanuel Noutahi,

I trust this message finds you well. I recently came across your article on the impressive performance of the representation SAFE in reverse molecular design. I have a few questions and would greatly appreciate your insights.

Firstly, in your comparison of the performance of different large pretrained models on molecules, I noticed the absence of MOLGPT, which is known for its exceptional performance. Given MOLGPT's ability to conduct conditional generation on targeted fragments or properties, my first question is about the performance comparison between SAFE and MOLGPT (e.g., Table 2).

Secondly, could you shed some light on the comparison between SAFE and MOLGPT in terms of their capabilities for conditional generation on targeted fragments or properties?

Lastly, I am curious about the choice not to employ conditional generation, as seen in MOLGPT, and instead adopt Proximal Policy Optimization (PPO) for goal-directed generative tasks. Additionally, it appears that the PPO-related programs are not open-sourced. Could you provide some insights into the rationale behind this choice?

Thank you in advance for your time and consideration. I look forward to hearing from you soon.

Best,

Yan Chen

Thanks for the amazing work. I found out that in certain cases the encoding and decoding of SMILES strings is not consistent. For example, for the canonical string:

'CC1CCC2(C)C(CCC3C2CCC2(C)C(C(=O)CO)CCC32)C1'

In the canonical form, this string only has 3 integers, despite having 4 ring systems. The generated fragment has again 4 digits. Thus, the assignment of the attachment integer fails. Once you decode the safe string, you end up with a different molecule that now has a 7 & 4 membered ring and not as in the original a 6 and 5 membered ring.

Here is a small code snippet to reproduce the issue:

import safe 
import datamol as dm
test_string = 'CC1CCC2(C)C(CCC3C2CCC2(C)C(C(=O)CO)CCC32)C1'

output_string = safe.decode(safe.encode(test_string))

print(output_string == test_string)
dm.viz.to_image([dm.to_mol(test_string), dm.to_mol(output_string)])

Original Molecule:

Decoded Molecule:

Benchmark data here: gs://valence-experiments/safe/drugs.csv

Hello, how to eval your model from vaildity, uniqueness and diversity?

I used your tutorial, but get 83% validity for De Novo generation.

Hi,
What is the easiest way to extract the embedding layer so we can get a 768-dim or x-dim for every molecule ?

The training dataset is being uploaded at https://huggingface.co/datasets/datamol-io/safe-gpt (62%).

I finally had to use.a GCP VM (order of magnitude faster for all the steps).

But HF has an hourly quota limit so I can only upload ~30% by ~30%. Should be done soon - will close here once it is.