AceGen: A TorchRL-based toolkit for reinforcement learning in generative chemistry

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Overview

ACEGEN is a comprehensive toolkit designed to leverage reinforcement learning (RL) techniques for generative chemistry tasks, particularly in drug design. ACEGEN harnesses the capabilities of TorchRL, a modern library for general decision-making tasks, to provide a flexible and integrated solution for generative drug design challenges.

The full paper can be found here.

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Features

• Multiple Generative Modes: ACEGEN facilitates the generation of chemical libraries with different modes: de novo generation, scaffold decoration, and fragment linking.
• RL Algorithms: ACEGEN offers task optimization with various reinforcement learning algorithms such as Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), Reinforce, Reinvent, and Augmented Hill-Climb (AHC).
• Pre-trained Models: The toolkit offers pre-trained models including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and GPT-2.
• Scoring Functions : ACEGEN relies on MolScore, a comprehensive scoring function suite for generative chemistry, to evaluate the quality of the generated molecules.
• Customization Support: ACEGEN provides tutorials for integrating custom models and custom scoring functions, ensuring flexibility for advanced users.

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Installation

Conda environment

To create the conda / mamba environment, run

conda create -n acegen python=3.10 -y
conda activate acegen
pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121

Install Tensordict

To install Tensordict, run

git clone https://github.com/pytorch/tensordict.git
cd tensordict
python setup.py install

Install TorchRL

To install TorchRL, run

git clone https://github.com/pytorch/rl.git
cd rl
python setup.py install

Install ACEGEN

To install ACEGEN, run (use pip install -e ./ for develop mode)

pip3 install tqdm wandb hydra-core
git clone https://github.com/Acellera/acegen-open.git
cd acegen-open
pip install ./

Optional dependencies

Unless you intend to define your own custom scoring functions, install MolScore by running

pip3 install MolScore

To use the scaffold decoration and fragment linking, install promptsmiles by running

pip3 install promptsmiles

To learn how to configure constrained molecule generation with AcGen and promptsmiles, please refer to this tutorial.

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Generating libraries of molecules

ACEGEN has multiple RL algorithms available, each in a different directory within the acegen-open/scripts directory. Each RL algorithm has three different generative modes of execution: de novo, scaffold decoration, and fragment linking.

Each mode of execution has its own configuration file in YAML format, located right next to the script. To modify training parameters for any mode, edit the corresponding YAML file. For a breakdown of the general structure of our configuration files, refer to this tutorial.

While the default values in the configuration files are considered sensible, a default scoring function and model architecture are also defined so users can test the scripts out of the box. However, users might generally want to customize the model architecture or the scoring function.

To customize the model architecture, refer to the [Changing the model architecture](##Changing the model architecture) section. To customize the scoring function, refer to the [Changing the scoring function](##Changing the scoring function) section.

Running training scripts to generate compoud libraries

To run the training scripts for denovo generation, run the following commands:

python scripts/reinforce/reinforce.py --config-name config_denovo
python scripts/a2c/a2c.py --config-name config_denovo
python scripts/ppo/ppo.py --config-name config_denovo
python scripts/reinvent/reinvent.py --config-name config_denovo
python scripts/ahc/ahc.py --config-name config_denovo

To run the training scripts for scaffold decoration, run the following commands (requires installation of promptsmiles):

python scripts/reinforce/reinforce.py --config-name config_scaffold
python scripts/a2c/a2c.py --config-name config_scaffold
python scripts/ppo/ppo.py --config-name config_scaffold
python scripts/reinvent/reinvent.py --config-name config_scaffold
python scripts/ahc/ahc.py --config-name config_scaffold

To run the training scripts for fragment linking, run the following commands (requires installation of promptsmiles):

python scripts/reinforce/reinforce.py --config-name config_linking
python scripts/a2c/a2c.py --config-name config_linking
python scripts/ppo/ppo.py --config-name config_linking
python scripts/reinvent/reinvent.py --config-name config_linking
python scripts/ahc/ahc.py --config-name config_linking

Advanced usage

Scripts are also available as executables after installation, but both the path and name of the config must be specified. For example,

ppo.py --config-path=<path_to_config_dir> --config-name=<config_name.yaml> 

YAML config parameters can also be specified on the command line. For example,

ppo.py --config-path=<path_to_config_dir> --config-name=<config_name.yaml> total_smiles=100

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Changing the scoring function

To change the scoring function, adjust the molscore parameter in any configuration files. Set it to point to a valid MolScore configuration file (e.g. ../MolScore/molscore/configs/GuacaMol/Albuterol_similarity.json). Alternatively, you can set the molscore parameter to the name of a valid MolScore benchmark (such as MolOpt, GuacaMol, etc.) to automatically execute each task in the benchmark. For further details on MolScore, please refer to the MolScore repository.

Alternatively, users can define their own custom scoring functions and use them in the ACEGEN scripts by following the instructions in this tutorial.

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Changing the model architecture

Available models

We provide a variety of default priors that can be selected in the configuration file. These include:

• A Gated Recurrent Unit (GRU) model

  • pre-training dataset1 (default): ChEMBL
  • pre-training dataset2: ZINC250k
  • number of parameters: 4,363,045
  • to select set the field model to gru in any configuration file

• A Long Short-Term Memory (LSTM) model

  • pre-training dataset: ChEMBL
  • number of parameters: 5,807,909
  • to select set the field model to lstm in any configuration file

• A GPT-2 model (requires installation of HuggingFace's transformers library)

  • pre-training dataset: REAL 350/3 lead-like, 613.86M cpds, CXSMILES
  • number of parameters: 5,030,400
  • to select set the field model to gpt2 in any configuration file

• A Mamba model (requires installation of mamba-ssm library)

  • pre-training dataset: ChEMBL
  • number of parameters: 2,809,216
  • to select set the field model to mamba in any configuration file

Integration of custom models

We also encourage users to integrate their own models into ACEGEN.

A detailed guide on integrating custom models can be found in this tutorial.

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Results on the MolOpt benchmark

Algorithm comparison for the Area Under the Curve (AUC) of the top 100 molecules on MolOpt benchmark scoring functions. Each algorithm ran 5 times with different seeds, and results were averaged. The default values for each algorithm are those in our de novo configuration files. Additionally, for Reinvent we also tested the configuration proposed in the MolOpt paper.

Task	REINFORCE	REINVENT	REINVENT MolOpt	AHC	A2C	PPO	PPOD
Albuterol_similarity	0.68	0.69	0.90	0.77	0.82	0.93	0.94
Amlodipine_MPO	0.55	0.56	0.65	0.56	0.55	0.58	0.68
C7H8N2O2	0.83	0.82	0.90	0.76	0.84	0.89	0.89
C9H10N2O2PF2Cl	0.70	0.70	0.76	0.68	0.69	0.66	0.79
Celecoxxib_rediscovery	0.63	0.64	0.77	0.72	0.73	0.65	0.82
DRD2	0.98	0.97	0.99	0.98	0.98	0.99	0.99
Deco_hop	0.63	0.63	0.67	0.64	0.62	0.62	0.66
Fexofenadine_MPO	0.71	0.71	0.80	0.72	0.71	0.73	0.78
GSK3B	0.84	0.84	0.92	0.82	0.85	0.90	0.92
JNK3	0.75	0.75	0.85	0.75	0.74	0.80	0.87
Median_molecules_1	0.26	0.24	0.36	0.24	0.31	0.33	0.35
Median_molecules_2	0.22	0.22	0.28	0.24	0.25	0.25	0.29
Mestranol_similarity	0.60	0.55	0.85	0.66	0.69	0.75	0.89
Osimertinib_MPO	0.82	0.82	0.86	0.83	0.81	0.82	0.84
Perindopril_MPO	0.48	0.47	0.54	0.47	0.48	0.50	0.53
QED	0.94	0.94	0.94	0.94	0.94	0.94	0.94
Scaffold_hop	0.80	0.79	0.86	0.80	0.80	0.80	0.84
Sitagliptin_MPO	0.34	0.33	0.38	0.33	0.39	0.32	0.39
Thiothixene_rediscovery	0.41	0.41	0.56	0.45	0.48	0.48	0.58
Troglitazone_rediscovery	0.31	0.31	0.47	0.34	0.35	0.46	0.52
Valsartan_smarts	0.03	0.02	0.02	0.02	0.02	0.03	0.03
Zaleplon_MPO	0.47	0.47	0.52	0.48	0.47	0.50	0.52
Total	13.67	13.60	15.65	13.91	14.27	14.65	15.80

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De Novo generation example: docking in the 5-HT2A

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Scaffold constrained generation example: BACE1 docking with AHC algorithm