promptsmiles 1.4.2

Description:

promptsmiles 1.4.2

PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models
This library contains code to manipulate SMILES strings to facilitate iterative prompting to be coupled with a trained chemical language model (CLM) that uses SMILES notation.
Installation
The libary can be installed via pip
pip install promptsmiles

Or via obtaining a copy of this repo, promptsmiles requires RDKit.
git clone https://github.com/compsciencelab/PromptSMILES.git
cd PromptSMILES
pip install ./

Use
PromptSMILES is designed as a wrapper to CLM sampling that can accept a prompt (i.e., an initial string to begin autoregressive token generation). Therefore, it requires two callable functions, described later. PromptSMILES has 3 main classes, DeNovo (a dummy wrapper to make code consistent), ScaffoldDecorator, and FragmentLinker.
Scaffold Decoration
from promptsmiles import ScaffoldDecorator, FragmentLinker

SD = ScaffoldDecorator(
scaffold="N1(*)CCN(CC1)CCCCN(*)",
batch_size=64,
sample_fn=CLM.sampler,
evaluate_fn=CLM.evaluater,
batch_prompts=False, # CLM.sampler accepts a list of prompts or not
optimize_prompts=True,
shuffle=True, # Randomly select attachment points within a batch or not
return_all=False,
)
smiles = SD.sample(batch_size=3, return_all=True) # Parameters can be overriden here if desired


Fragment linking / scaffold hopping
FL = FragmentLinker(
fragments=["N1(*)CCNCC1", "C1CC1(*)"],
batch_size=64,
sample_fn=CLM.sampler,
evaluate_fn=CLM.evaluater,
batch_prompts=False,
optimize_prompts=True,
shuffle=True,
scan=False, # Optional when combining 2 fragments, otherwise is set to true
return_all=False,
)
smiles = FL.sample(batch_size=3)


Required chemical language model functions
Notice the callable functions required CLM.sampler and CLM.evaluater. The first is a function that samples from the CLM given a prompt.
def CLM_sampler(prompt: Union[str, list[str]], batch_size: int):
"""
Input: Must have a prompt and batch_size argument.
Output: SMILES [list]
"""
# Encode prompt and sample as per model implementation
return smiles

Note: For a more efficient implementation, prompt should accept a list of prompts equal to batch_size and batch_prompts should be set to True in the promptsmiles class used.
The second is a function that evaluates the NLL of a list of SMILES
def CLM_evaluater(smiles: list[str]):
"""
Input: A list of SMILES
Output: NLLs [list, np.array, torch.tensor](CPU w.o. gradient)
"""
return nlls