BioNeMo Framework Tutorials

The best way to get started with BioNeMo Framework is with the tutorials. Below are some of the example walk-throughs/tutorials which contains code snippets that you can run from within the BioNeMo container.

Some of the tutorials are presented in the format of Markdown with Python or shell codes, where a user can run example cells by copying the code to a script in an appropriate folder inside the BioNeMo container and execute it.

Other tutorial files are provided in the MarkDown (.md) format, where the file may contain various example code snippets in YAML/bash/python/etc. formats. You can follow the instructions provided in those files, make appropriate changes to the codes, and execute them once the container is launched.

In both types of tutorials, it is convenient to first launch the BioNeMo Framework container, and copy the tutorial files to the container – either via the Jupyter-Lab interface drag-and-drop, or by mounting the files during the launch of container (docker run -v ...)

Topic	Title
Model Pre-Training	Launching a MegaMolBART model pre-training with ZINC-15 dataset
Custom Datasets	Setting up the ZINC15 dataset used for training MolMIM
Model Pre-Training	Launching a MolMIM model pre-training with ZINC-15 dataset, both from scratch and starting from an existing checkpoint
Model Pre-Training	Launching an ESM-1nv model pre-training with UniRef50 dataset
Model Pre-Training	Launching an ESM-2nv model pre-training with curated data from UniRef50, UniRef90
Model Training	Launching an EquiDock model pre-training with DIPS or DB5 datasets
Inference	Performing Inference with MegaMolBART for Generative Chemistry and Predictive Modeling with RAPIDS
Inference	Performing Inference with MolMIM for Generative Chemistry and Predictive Modeling with RAPIDS
Inference	Performing Inference with ESM1-nv and Predictive Modeling with RAPIDS
Inference	Performing Inference with ESM2-nv and Predictive Modeling with RAPIDS
Inference	Performing Property-guided Molecular Optimization with MolMIM, which internally involves inference
Model Finetuning	Overview of Finetuning pre-trained models in BioNeMo
Encoder Finetuning	Encoder Fine-tuning in BioNeMo: MegaMolBART
Downstream Tasks	Training a Retrosynthesis Model using USPTO50 Dataset
Downstream Tasks	Fine-tuning MegaMolBART for Solubility Prediction
Custom Datasets	Adding the OAS Dataset: Downloading and Preprocessing
Custom Datasets	Adding the OAS Dataset: Modifying the Dataset Class
Custom DataLoaders	Creating a Custom Dataloader