:safety_vest: SAFE

Sequential Attachment-based Fragment Embedding (SAFE) is a novel molecular line notation that represents molecules as an unordered sequence of fragment blocks to improve molecule design using generative models.

Paper | Docs | 🤗 Model | 🤗 Training Dataset

Overview of SAFE

SAFE is the deep learning molecular representation. It's an encoding leveraging a peculiarity in the decoding schemes of SMILES, to allow representation of molecules as a contiguous sequence of connected fragments. SAFE strings are valid SMILES strings, and thus are able to preserve the same amount of information. The intuitive representation of molecules as an ordered sequence of connected fragments greatly simplifies the following tasks often encountered in molecular design:

• de novo design
• superstructure generation
• scaffold decoration
• motif extension
• linker generation
• scaffold morphing.

The construction of a SAFE strings requires defining a molecular fragmentation algorithm. By default, we use [BRICS], but any other fragmentation algorithm can be used. The image below illustrates the process of building a SAFE string. The resulting string is a valid SMILES that can be read by datamol or RDKit.

News 🚀

💥 2024/01/15 💥

1. @IanAWatson has a C++ implementation of SAFE in LillyMol that is quite fast and use a custom fragmentation algorithm. Follow the installation instruction on the repo and checkout the docs of the CLI here: docs/Molecule_Tools/SAFE.md

Installation

You can install safe using pip:

pip install safe-mol

You can use conda/mamba:

mamba install -c conda-forge safe-mol

2024/11/22

NOTE: Installation might cause issues like no detection of GPUs (which can be checked by torch.cuda.is_available()) and sengmentation error due to mismatch between installed and driver cuda versions. In that case, follow these steps:

Create a new environment using conda:

conda create -n env_safe python=3.12
conda activate env_safe

Check nvidia driver version on machine by running nvcc --version or nvidia-smi commands

Install pytorch with compatible cuda versions (from https://pytorch.org/get-started/locally/) and safe-mol:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -c conda-forge safe-mol

Datasets and Models

Usage

Please refer to the documentation, which contains tutorials for getting started with safe and detailed descriptions of the functions provided, as well as an example of how to get started with SAFE-GPT.

API

We summarize some key functions provided by the safe package below.

Examples

Translation between SAFE and SMILES representations

import safe

ibuprofen = "CC(Cc1ccc(cc1)C(C(=O)O)C)C"

# SMILES -> SAFE -> SMILES translation
try:
    ibuprofen_sf = safe.encode(ibuprofen)  # c12ccc3cc1.C3(C)C(=O)O.CC(C)C2
    ibuprofen_smi = safe.decode(ibuprofen_sf, canonical=True)  # CC(C)Cc1ccc(C(C)C(=O)O)cc1
except safe.EncoderError:
    pass
except safe.DecoderError:
    pass

ibuprofen_tokens = list(safe.split(ibuprofen_sf))

Training/Finetuning a (new) model

A command line interface is available to train a new model, please run safe-train --help. You can also provide an existing checkpoint to continue training or finetune on you own dataset.

For example:

safe-train --config