Welcome to the fascinating world of 3D molecular design! In this article, we will guide you on how to utilize the MolGym repository to train reinforcement learning policies for designing molecules in a 3D space. Understanding this process can open new doors in molecular design and discovery. Let’s dive in!

Understanding the Basics

The MolGym repository allows an agent to build molecules by taking atoms from a specific “bag” and placing them onto a 3D “canvas.” Imagine you are an artist working with colored blocks instead of paint; each colored block represents an atom, and your canvas is the 3D space where you create your masterpiece. The agent repeatedly chooses atoms from the bag and places them on the canvas, creating unique molecular structures.

Setting Up MolGym

To get started, you will need to set up your environment. Follow these steps to ensure you have all the necessary dependencies:

• Python = 3.7
• ase
• cormorant
• gym
• matplotlib
• pandas
• quadpy
• schnetpack
• sparrow = 2.0.1
• torch = 1.5.1
• torch-scatter = 2.0.5

Installing Required Packages

Use the following commands to install the necessary packages:

pip install -r requirements.txt
pip install -e .

Ensure that the CUDA versions associated with torch and torch-scatter are compatible. If you encounter any issues, refer to the documentation.

Installing Sparrow

To install Sparrow, use the conda package manager:

conda config --add channels conda-forge
conda config --set channel_priority strict
conda install scine-sparrow-python

Training Your Model

Once your setup is complete, it’s time to train your model. Follow the command outlined below to start a single-bag experiment with the molecule SF6:

python3 scripts/run.py \
--name=SF6 \
--symbols=X,F,S \
--formulas=SF6 \
--min_mean_distance=1.10 \
--max_mean_distance=2.10 \
--bag_scale=5 \
--beta=-10 \
--model=covariant \
--canvas_size=7 \
--num_envs=10 \
--num_steps=15000 \
--num_steps_per_iter=140 \
--mini_batch_size=140 \
--save_rollouts=eval \
--device=cuda \
--seed=1

Hyper-parameters for additional experiments can be found in the referenced papers.

Evaluating Your Model

To visualize how well your model is learning, generate learning curves by running the following command:

python3 scripts/plot.py --dir=results

This will automatically produce a figure of the learning curve. To retrieve the generated molecular structures, use the command below:

python3 scripts/structures.py --dir=data --symbols=X,F,S

You can visualize the structures in the XYZ file using software like PyMOL.

Troubleshooting

If you encounter any problems during installation or execution, here are some troubleshooting tips:

• Verify that your Python version is correct.
• Ensure that all dependencies are properly installed.
• If issues arise with CUDA, check version compatibility.
• Consult the documentation for specific issues related to torch-scatter.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

In conclusion, MolGym offers an innovative platform for applying reinforcement learning techniques to 3D molecular design. By following this guide, you can set up your environment, train your models, and visualize learning outcomes effectively. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.