ChemTSv2

ChemTSv2 is a refined and extended version of ChemTS¹ and MPChemTS². The original implementations are available at https://github.com/tsudalab/ChemTS and https://github.com/yoshizoe/mp-chemts, respectively.

ChemTSv2 provides:

easy-to-run interface by using only a configuration file
easy-to-define framework for users' any reward function, molecular filter, and tree policy
various usage examples in the GitHub repository

How to setup 📌

Requirements 📝

Click to show/hide requirements

python: 3.7
rdkit: 2021.03.5
tensorflow: 2.5.0
pyyaml
pandas
joblib
mpi4py: 3.0.3 (for massive parallel mode)

ChemTSv2 with single process mode 🚗

Click to show/hide the instruction

cd YOUR_WORKSPACE
python3.7 -m venv .venv
source .venv/bin/activate
pip install --upgrade chemtsv2

ChemTSv2 with massive parallel mode ✈️

Click to show/hide the instruction

NOTE: You need to run ChemTSv2-MP on a server where OpenMPI or MPICH is installed. If you can't find `mpiexec` command, please consult your server administrator to install such an MPI library.

If you can use/prepare a server with MPI environment, please follow the (a) instruction; otherwise, please follow the (b) instruction.

(a) Installation on a server WITH a MPI environment

cd YOUR_WORKSPACE
python3.7 -m venv .venv
source .venv/bin/activate
pip install --upgrade chemtsv2
pip install mpi4py==3.0.3

(b) Installation on a server WITHOUT a MPI environment

conda create -n mpchem python=3.7
# swith to the `mpchem` environment
conda install -c conda-forge openmpi cxx-compiler mpi mpi4py=3.0.3
pip install --upgrade chemtsv2

How to run ChemTSv2 📌

1. Clone this repository and move into it

git clone [email protected]:molecule-generator-collection/ChemTSv2.git
cd ChemTSv2

2. Prepare a reward file

Please refer to reward/README.md. An example of reward definition for LogP maximization task is as follows.

from rdkit.Chem import Descriptors
import numpy as np
from reward.reward import Reward

class LogP_reward(Reward):
    def get_objective_functions(conf):
        def LogP(mol):
            return Descriptors.MolLogP(mol)
        return [LogP]
    
    def calc_reward_from_objective_values(objective_values, conf):
        logp = objective_values[0]
        return np.tanh(logp/10)

3. Prepare a config file

The explanation of options are described in the Support option/function section. The prepared reward file needs to be specified in reward_setting. For details, please refer to a sample file (config/setting.yaml). If you want to pass any value to calc_reward_from_objective_values (e.g., weights for each value), add it in the config file.

4. Generate molecules

ChemTSv2 with single process mode 🚗

chemtsv2 -c config/setting.yaml

ChemTSv2 with massive parallel mode ✈️

mpiexec -n 4 chemtsv2-mp --config config/setting_mp.yaml

Example usage 📌

Target	Reward	Config	Additional requirement	Ref.
LogP	logP_reward.py	setting.yaml	-	-
Jscore	Jscore_reward.py	setting_jscore.yaml	-	¹
Absorption wavelength	chro_reward.py	setting_chro.yaml	Gaussian 16³ via QCforever⁴	⁵
Upper-absorption & fluorescence wavelength	fluor_reward.py	setting_fluor.yaml	Gaussian 16³ via QCforever⁴	⁶
Kinase inhibitory activities	dscore_reward.py	setting_dscore.yaml	LightGBM⁷	⁸
Docking score	Vina_binary_reward.py	setting_vina_binary.yaml	AutoDock Vina⁹	¹⁰

Support option/function 📌

Option	Single process	Massive parallel	Description
`c_val`	✅	✅	Exploration parameter to balance the trade-off between exploration and exploitation. A larger value (e.g., 1.0) prioritizes exploration, and a smaller value (e.g., 0.1) prioritizes exploitation.
`threshold_type`	✅	✔️	Threshold type to select how long (`hours`) or how many (`generation_num`) molecule generation to perform. Massive parallel mode currently supports only the how long (`hours`) option.
`hours`	✅	✅	Time for molecule generation in hours
`generation_num`	✅	⬜	Number of molecules to be generated. Please note that the specified number is usually exceeded.
`expansion_threshold`	✅	⬜	(Advanced) Expansion threshold of the cumulative probability. The default is set to 0.995.
`simulation_num`	✅	⬜	(Advanced) Number of rollout runs in one cycle of MCTS. The default is set to 3.
`flush_threshold`	✅	⬜	Threshold for saving the progress of a molecule generation. If the number of generated molecules exceeds the threshold value, the result is saved. The default is set to -1, and this represents no progress is to be saved.
Molecule filter	✅	✅	Molecule filter to skip reward calculation of unfavorable generated molecules. Please refer to filter/README.md for details.
RNN model replacement	✅	✅	Users can switch RNN models used in expansion and rollout steps of ChemTSv2. The model needs to be trained using Tensorflow. `model_json` specifies the JSON file that contains the architecture of the RNN model, and `model_weight` specifies the file in H5 format that contains a set of the values of the weights.
Reward replacement	✅	✅	Users can use any reward function as long as they follow the reward base class (reward/reward.py). Please refer to reward/README.md for details.
Policy replacement	✅	⬜	(Advanced) Users can use any policy function as long as they follow the policy base class (policy/policy.py). Please refer to policy/README.md for details.
Restart	🔰	⬜	Users can save the checkpoint file and restart from the file. If users want to save a checkpoint file, set `save_checkpoint` to True and specify the file name in `checkpoint_file`. If users want to restart from the checkpoint, set `restart` to True and specify the checkpoint file in `checkpoint_file`.

✅ indicates that the option/function is supported.
✔️ indicates that the option/function is partially supported.
🔰 indicates that the option/function is beta version.
⬜ indicates that the option/function is NOT supported.

Filter functions are described in filter/README.md.

Advanced usege 📌

Extend user-specified SMILES

You can extend the SMILES string you input. In this case, you need to put the atom you want to extend at the end of the string and run ChemTS with --input_smiles argument as follows.

chemtsv2 -c config/setting.yaml --input_smiles 'C1=C(C)N=CC(N)=C1C'

GPU acceleration

If you want to use GPU, run ChemTS with --gpu GPU_ID argument as follows.

chemtsv2 -c config/setting.yaml --gpu 0

License 📌

This package is distributed under the MIT License.

Contact 📌

Shoichi Ishida ([email protected])
Kei Terayama ([email protected]).

Yang, X., Zhang, J., Yoshizoe, K., Terayama, K., & Tsuda, K. (2017). ChemTS: an efficient python library for de novo molecular generation. Science and Technology of Advanced Materials, 18(1), 972–976. https://doi.org/10.1080/14686996.2017.1401424 ↩ ↩²
Yang, X., Aasawat, T., & Yoshizoe, K. (2021). Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design. In International Conference on Learning Representations. https://openreview.net/forum?id=6k7VdojAIK ↩
Frisch, M. J. et al. Gaussian 16 Revision C.01. 2016; Gaussian Inc. Wallingford CT. ↩ ↩²
Sumita, M., Terayama, K., Tamura, R., & Tsuda, K. (2022). QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization. Journal of Chemical Information and Modeling, 62(18), 4427–4434. https://doi.org/10.1021/acs.jcim.2c00812 ↩ ↩²
Sumita, M., Yang, X., Ishihara, S., Tamura, R., & Tsuda, K. (2018). Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies. ACS Central Science, 4(9), 1126–1133. https://doi.org/10.1021/acscentsci.8b00213 ↩
Sumita, M., Terayama, K., Suzuki, N., Ishihara, S., Tamura, R., Chahal, M. K., Payne, D. T., Yoshizoe, K., & Tsuda, K. (2022). De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning. Science Advances, 8(10). https://doi.org/10.1126/sciadv.abj3906 ↩
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., … Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. ↩
Yoshizawa, T., Ishida, S., Sato, T., Ohta, M., Honma, T., & Terayama, K. (2022). Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. Journal of Chemical Information and Modeling, 62(22), 5351–5360. https://doi.org/10.1021/acs.jcim.2c00787 ↩
Eberhardt, J., Santos-Martins, D., Tillack, A. F., & Forli, S. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling, 61(8), 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203 ↩
Ma, B., Terayama, K., Matsumoto, S., Isaka, Y., Sasakura, Y., Iwata, H., Araki, M., & Okuno, Y. (2021). Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. Journal of Chemical Information and Modeling, 61(7), 3304–3313. https://doi.org/10.1021/acs.jcim.1c00679 ↩

marcel-n / chemtsv2 Goto Github PK

chemtsv2's Introduction

ChemTSv2

How to setup 📌

Requirements 📝

ChemTSv2 with single process mode 🚗

ChemTSv2 with massive parallel mode ✈️

(a) Installation on a server WITH a MPI environment

(b) Installation on a server WITHOUT a MPI environment

How to run ChemTSv2 📌

1. Clone this repository and move into it

2. Prepare a reward file

3. Prepare a config file

4. Generate molecules

ChemTSv2 with single process mode 🚗

ChemTSv2 with massive parallel mode ✈️

Example usage 📌

Support option/function 📌

Advanced usege 📌

Extend user-specified SMILES

GPU acceleration

License 📌

Contact 📌

Footnotes

chemtsv2's People

Contributors

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