This repository contains various notes on using ML tools for protein structure, engineering & design, property prediction, and related topics. It is not intended to serve as introductory material. These notes are not comprehensive and may have errors. If you find any errors, or would like to contribute something you feel is missing, please contact me on GitHub or on LinkedIn.
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Browse by topic
- Biophysics — Physical properties and mechanisms connecting protein sequence, structure, dynamics, binding, and catalysis.
- Evolution — Selection, mutation effects, ancestry, and homology across natural and engineered proteins.
- Antibodies — Antibody architecture, repertoires, maturation, antigen recognition, and single-domain antibodies.
- Prediction — What can be predicted about proteins, and how confidence relates to the quantity being predicted.
- Design — Engineering sequences, folds, binding, catalytic function, and properties that make proteins useful.
- Evidence — Dataset composition, generalization, experimental validation, and the interpretation of measurements.
- Cell biology — Immune signaling and cellular mechanisms relevant to protein therapeutics.
- Training — How models are trained and adapted: pretraining, fine-tuning, objectives, optimization, and scaling.
- Inference — How to obtain useful outputs from existing models through conditioning, feature extraction, sampling, guidance, and ensembling.
- Model design — How models are constructed: network architectures, multimodal representations, and generative formulations.
- Model analysis — What models represent internally, how that information is organized, and how it affects their behavior.