Supporting code for the paper «Leveraging molecular structure and bioactivity with chemical language models for drug design»
Abstract of the paper: Generative chemical language models (CLMs) can be used for de novo molecular structure generation. These CLMs learn from the structural information of known molecules to generate new ones. In this paper, we show that “hybrid” CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), we created a large collection of virtual molecules with a generative CLM. This primary virtual compound library was further refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ binders and non-binders by transfer learning. Several of the computer-generated molecular designs were commercially available, which allowed for fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design in low-data situations.
First, you need to clone the repository:
git clone [email protected]:michael1788/hybridCLMs.git
Then, you can run the following command, which will create a conda virtual environment and install all the needed packages (if you don't have conda, you can follow the instructions to install it here).
cd hybridCLMs/
conda env create -f environment.yml
Once the installation is done, you can activate the virtual conda environment for this project:
conda activate hybrid
Please note that you will need to activate this virtual conda environment every time you want to use this project.
You can run an example experiment based on the data used in the paper by following the procedure described in A and B.
Note: we provide the pretrained weights for both the CLM and the E-CLM. You can therefore skip steps A1, A2, and B1 if you do not want to repeat the whole experiment, or if you do not have access to at least one GPU.
A. Generate the focused chemical library
We start by generating the focused virtual chemical library in part A.
A1. Process the data to train the chemical language model (CLM):
cd experiments/
sh run_processing.sh configfiles/clm/A01_clm.ini
Note: you can skip this step, as we provide the processed pretraining data.
A2. Pretrain the CLM:
sh run_training.sh configfiles/clm/A01_clm.ini
Note: we encourage you to skip this step, and to use the available pretrained model, especially if you do not have a GPU.
A3. Process the data to fine-tune the CLM:
sh run_processing.sh configfiles/ft_clm_generation/A01_clm_ft.ini
A4. Fine-tune the pretrained CLM:
sh run_training.sh configfiles/ft_clm_generation/A01_clm_ft.ini
A5. Generate SMILES strings with the fine-tuned CLM:
sh run_generation.sh configfiles/ft_clm_generation/A01_clm_ft.ini
Note: this step will be slow if you sample 5,000 SMILES strings by epoch as specified in A01_clm_ft.ini without a GPU. We advised you to first try with 500 SMILES strings (to do so, you can update the value in A01_clm_ft.ini).
A6. Process the generated SMILES strings to get the new molecules to constitute the focused virtual chemical library:
sh run_novo.sh configfiles/ft_clm_generation/A01_clm_ft.ini
B. Refine the focused chemical library
In part B, we refine the focused virtual chemical library by leveraging the bioactivity data of the fine-tuning set.
B1. Pretrain the E-CLM:
sh run_training.sh configfiles/eclm/A01_eclm.ini
Note: we encourage you to skip this step, and to use the available pretrained model, especially if you do not have a GPU.
Next, we fine-tune the pretrained E-CLM to do the ordinal classification. We start with a cross-validation to find a suitable set of hyperparameters (e.g. the number of fine-tuning epochs).
B2. Run a cross-validation experiment:
sh run_experiment.sh configfiles/ft_eclm/A01_cv.ini
B3. Run the analysis of the cross-validation experiment:
sh run_analysis.sh configfiles/ft_eclm/A01_cv.ini
You will find in hybridCLMs/experiments/outputs/ft_eclm/A01_cv/analysis/ a plot with your results, as well as a .csv file version of it.
Note: you can explore hyperparameters by running commands B2 and B3 on other configuration files, where you can change the hyperparameters you want to explore (e.g. the learning rate or the number of fine-tuning epochs).
B4. Once you are satisfied with your cross-validation experiment(s), you can run the E-CLM once on the test set to assess the final performance of your hyperparameters. For example, for the hyperparameters we used in the cross-validation in B2 and B3:
sh run_experiment_test_set.sh configfiles/ft_eclm/A02_test.ini
B5. And run again the analysis:
sh run_analysis_test_set.sh configfiles/ft_eclm/A02_test.ini
B6. You can now train on all the data the best E-CLM, as defined by the results on the test set. This will create an ensemble of models (with the number of models specified in the configuration file), which will be used for deployment, i.e. to make predictions on the focused virtual chemical library:
sh run_experiment_alldata.sh configfiles/ft_eclm/A03_alldata.ini
B7. Finally, we can use the deep ensemble of E-CLMs to refine the focused virtual chemical library generated in A.:
sh run_ensemble.sh configfiles/ensemble/A01_ensemble.ini
The results of the ensemble prediction can be found in a .csv file: hybridCLMs/experiments/outputs/ensemble/A01_ensemble/.
@article{Moret2021,
title={Leveraging molecular structure and bioactivity with chemical language models for drug design},
author={Moret, Michael and Grisoni, Francesca and Brunner, Cyrill and Schneider, Gisbert},
journal={Preprint at https://chemrxiv.org/engage/chemrxiv/article-details/615580ced1fc334326f9356e},
year={2021},
}
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