BioNeMo Framework Tutorials

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

Tutorials are presented as notebooks (.ipynb format), which may contain various code snippets in formats like Python, Bash, YAML, etc. You can follow the instructions in these files, make appropriate code changes, and execute them in the container.

It is convenient to first launch the BioNeMo Framework container and copy the tutorial files to the container, either via the JupyterLab interface drag-and-drop or by mounting the files during the launch of the 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	ESM-1nv: Data preprocessing and model pre-training using BioNeMo with curated data from UniRef50, UniRef90
Model Pre-Training	ESM-2nv: Data Preprocessing and Model Training
Model Pre-Training	Pretraining a Geneformer model for representing single cell RNA-seq data
Geneformer Benchmarking	Benchmarking pre-trained Geneformer models against a baseline with cell type classification
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	Zero-Shot Protein Design Using ESM-2nv
Inference	Performing Inference with ESM-2nv and Predictive Modeling with RAPIDS
Inference	MolMIM Inferencing for Generative Chemistry and Downstream Prediction
Inference	Performing Property-guided Molecular Optimization with MolMIM, which internally involves inference
Inference	Performing inference and cell clustering on CELLxGENE data with a pretrained geneformer model
Inference	Performing inference on OAS sequences with ESM-2nv
Model Finetuning	Overview of Finetuning pre-trained models in BioNeMo
Model Finetuning	Fine-Tune ESM-2nv on FLIP Data for Sequence-Level Classification, Regression, Token-Level Classification, and with LoRA Adapters
Model Pre-Training and Finetuning	Pretrain from Scratch, Continue Training from an Existing Checkpoint, and Fine-tune ESM-2nv on Custom Data
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
Inference	Creating and Visualizing Embeddings with DNABERT
Model Finetuning	Pretrain, Fine-tune, and Perform Inference with DNABERT for Splice Site Prediction