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

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 ESM1-nv and Predictive Modeling with RAPIDS

Inference

Performing Inference with ESM2-nv and Predictive Modeling with RAPIDS

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