Hi everyone,

I just open-sourced a new bio-computational pipeline designed for Chronic Myeloid Leukemia (CML) drug optimization. The framework focuses on maximizing Imatinib binding affinity within the BCR-ABL1 kinase domain.

Key Features:

\* ESMFold Integration: Automated 3D atomic coordinate generation via Meta's ESMFold.

\* Deterministic Fallback: Local biomimetic backbone algorithm forcing real alpha-helix parameters if the API times out.

\* JAX-Accelerated Engine: Parallel genetic optimization loop compiled via JAX XLA linear kernel fusion to eliminate bottlenecks.

\* AutoDock Vina Automation: Dynamic center-of-mass mapping to initialize deep structural screening.

\* Active Site Protection: Hard-coded 'Absolute Protection Mask' locking amino acid positions 20-40 and 110-160 to shield the native binding cavity.

The standard experimental run successfully hits a final binding affinity of -9.79 kcal/mol.

Repository:

[https://github.com/tatopenn-cell/Dense-Evolution-Molecular-Pipeline](https://github.com/tatopenn-cell/Dense-Evolution-Molecular-Pipeline)

This project is fully open-source, and I want to be completely honest: I do not consider myself a professional chemist. I built this out of a genuine passion for computational biology and a desire to contribute, in my own small way, to open scientific research and help make the world a bit better. 

Because of this, I would absolutely love to connect with you all. I am highly open to discussion, feedback, and collaboration. Whether you have thoughts on the JAX optimization approach, suggestions on expanding the structural fallback mechanics, or advice on the chemistry side, please let me know. Let's improve this together. Thanks.





[Open Source] Automated pipeline targets BCR-ABL1 for CML drug optimization. Integrates ESMFold 3D predictions with AutoDock Vina, reaching a -9.79 kcal/mol binding affinity benchmark.  Check out the repo: [https://github.com/tatopenn-cell/Dense-Ev]