Deep learning for protein engineering with Ray - Stanley Bishop DeepChem.io

We will discuss Ray as an active learning orchestrator for protein engineering in the drug/medicine discovery process. In particular, we will look at the deployment of systems that involve active-learning feedback between sequence-to-sequence transformers, Alphafold driven sequence-to-structure prediction, and, more broadly, how these two deep learning methods are revolutionizing the field.

Stanley Bishop is an ML-nerd contributor to the open source-project DeepChem.io, which works to democratize deep learning for science.

Transcript

1. Stanley Page 1 Ray & Protein Engineering @ DeepChem
2. Stanley Page 2 • Mathematician working as a machine learning scientist • Developer at deepchem.io, an open-source project to democratize deep learning for science ✨come check us out✨ • Mostly made of proteins
3. Stanley Page 3 • The DeepChem project works to democratize deep learning for science • The DeepChem project aims to create high quality, open source tools for drug discovery, materials science, quantum chemistry, and biology. • DeepChem projects are managed by a group of open source contributors.
4. Stanley Page 4 • Proteins are complex molecules that perform critical functions in our bodies • Proteins are synthesized through the processes of transcription and translation • Protein synthesis is the bioinformatic equivalent to a compiler process
5. Stanley Page 4 • Proteins are complex molecules that perform critical functions in our bodies • Proteins are synthesized through the processes of transcription and translation • Protein synthesis is the bioinformatic equivalent to a compiler process
6. Stanley Page 5 • Proteins are made of amino acids • Amino acid compositions are deterministic and combinatorial • Imagine Lego bricks
7. Stanley Page 6 • Protein structure is the result of a three-part process • Until recently, these structures were largely observed experimentally • AlphaFold has changed the game
9. Stanley Page 8 • Protein complexes can perform predictable actions in response to stimulus • Molecules activate a protein by ‘docking’ which changes its electrochemical dynamics • The dynamics of this docking are of crucial importance to medicine discovery and disease treatment
10. Stanley Page 9 • In the 1950s there were tens of thousands birth defects caused by Thalidomide prescriptions to expecting mothers • Thalidomide-TBX5 docking complex yields reactive oxygen species • With predictive AI technologies, these reactions can be found prior to human testing
11. • The space of possible ligand molecules can contain hundreds of millions to billions of potential compounds • Per ligand computations are relatively expensive • Protein ligand docking presents a challenging distributed computing problem • That has been traditionally solved with on-prem hardware Stanley Page 10
12. Stanley Page 11 Docking is a DAG!
13. Stanley Page 12 • Task 1: generate 3D structures
14. Stanley Page 13 • Task 2: generate features
15. Stanley Page 14 • Task 3: compute dock complex
16. Stanley Page 15
17. Stanley Page 16 • Active learning has the potential to accelerate molecular simulation • Teams at Stanford and Harvard are using Ray for this purpose • Early results indicate a 10x improvement in computational efficiency
18. • The DeepChem is always looking for contributors. Check us out at DeepChem.io • Bioinformatics is likely to soon lead the machine learning charge in terms of the data scale of models… so there will be a lot to build • Deep gratitude to the Ray community for building such important infostructure for the machine learning revolution