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Announcement Guide: diff-biophys

This document outlines the strategy and drafted verbiage for announcing DiffBiophys on Twitter (X) and Reddit.

Because diff-biophys is a highly technical, JAX-based library targeting researchers, the messaging focuses on differentiability, gradient descent optimization, and physics-informed AI.

🐦 Twitter (X) Strategy: The "AI for Science" Hook

Target Audience: AI/ML Researchers, Structural Biologists, JAX Enthusiasts. Goal: High engagement through visuals and highlighting the "missing link" between structures and experimental data.

Option 1: The Visual Demo (Recommended)

Requires a GIF or short video showing a structure moving to fit a SAXS/NMR curve.

Draft Tweet:

AlphaFold gives us static structures, but biology happens in solution. 🧬

Introducing DiffBiophys: A hardware-accelerated, differentiable biophysics engine built in JAX.

Optimize protein models directly against experimental SAXS and NMR data using gradient descent. 📉

Repo: [Link] Docs: [Link] #AI4Science #StructuralBiology #JAX #MachineLearning

Option 2: The "Physics-Informed" AI Hook

Draft Tweet:

Training a protein representation model? Stop relying purely on sequence data.

DiffBiophys provides differentiable SAXS and NMR kernels in JAX, allowing you to use real-world solution-state physics as a loss function during model training. 🧠⚡

Check it out here: [Link] #DeepLearning #JAX #Bioinformatics #CompBio

👽 Reddit Strategy: Focused Subreddits

1. r/MachineLearning & r/learnmachinelearning

Tag: [P] (Project) Title: [P] DiffBiophys: Differentiable SAXS & NMR kernels in JAX for physics-informed protein AI

Post Content: Hi everyone,

I’ve released DiffBiophys, a high-performance Python library built on JAX that re-implements core structural biology observables (SAXS, NMR) as hardware-accelerated, auto-differentiable kernels.

The Problem: We have amazing structure prediction models (AlphaFold, ESMFold), but fitting these models or training new architectures against real-world, solution-state experimental data (like X-ray scattering) is computationally expensive and traditionally non-differentiable.

The Solution: DiffBiophys provides a "differentiable bridge." Because everything is written in JAX, you can:

  1. Optimize protein structures directly against experimental spectra via gradient descent (no massive MD simulations needed).
  2. Train GNNs or Diffusion models using physics-informed loss functions (e.g., penalize a model if its predicted structure doesn't match a known SAXS curve).
  3. Accelerate large-scale biophysical simulations on GPUs and TPUs.

Features:

  • Differentiable NeRF (Internal to Cartesian coordinates)
  • $O(N^2)$ Debye Formula for SAXS (GPU-optimized)
  • Differentiable NMR observables

Links:

  • GitHub: [Link to Repo]
  • Use Cases & Docs: [Link to Docs]

Would love to hear thoughts from anyone working at the intersection of AI and structural biology!

2. r/bioinformatics & r/StructuralBiology

Title: [Tool] DiffBiophys: Gradient-based structure refinement against SAXS and NMR data (built in JAX)

Post Content: Hi everyone,

I've been working on DiffBiophys, a new tool designed to bridge the gap between static structural models and experimental solution-state data.

If you've ever wanted to subtly refine a predicted structure (or an ensemble) to better fit a SAXS curve or NMR data, DiffBiophys allows you to do this directly using gradient descent.

Key Features:

  • Differentiable Physics: Calculates SAXS and NMR observables in a way that allows gradients to backpropagate directly to the atomic coordinates or backbone torsions.
  • Hardware Accelerated: Built on JAX, so it runs incredibly fast on GPUs/TPUs compared to traditional CPU-bound tools.
  • Pythonic API: Designed to integrate easily into existing Python data pipelines or Jupyter notebooks.

Get Started: pip install diff-biophys

Links:

  • GitHub: [Link to Repo]
  • Documentation & Tutorials: [Link to Docs]

I'm currently looking for feedback on the API and any specific observables you'd like to see added next!

3. r/JAX

Title: I built a differentiable biophysics engine for structural biology entirely in JAX

Post Content: Hey r/JAX,

I wanted to share a domain-specific application of JAX I've been working on: DiffBiophys.

It implements core structural biology calculations (like the Debye formula for X-ray scattering and spatial coordinate transforms like NeRF) as auto-differentiable, jit-compilable kernels.

This allows researchers to optimize 3D protein structures against experimental data using gradient-based optimizers (like Optax) instead of relying on stochastic sampling or rigid-body fitting.

It was a great experience translating traditional $O(N^2)$ biophysics algorithms into vectorized JAX primitives. If you're interested in AI for Science or just want to see some applied JAX code, check out the repo!

GitHub: [Link to Repo]

📈 Pre-Launch Checklist

  1. Polish the "Use Cases": Ensure the docs have at least one compelling Google Colab notebook demonstrating a gradient descent optimization.
  2. Create a Visual: Generate a GIF or plot comparing a "Before Optimization" and "After Optimization" fit for a SAXS curve.
  3. Check the Build: Ensure pip install diff-biophys works cleanly on a fresh environment.