Question: Exploring the Optimal Range for Denoising Timesteps (Iterations) in Binder Design

Dear RFdiffusion Developers

First, my sincere thanks for creating such a powerful tool for protein design. I am writing to seek your expert guidance on a subtle observation we've made regarding the iterations hyperparameter (diffuser.T) for a de novo protein binder design task.

Observation: In our experiments, we have found that a higher number of iterations does not necessarily guarantee a better success rate. Specifically, we observed: A run with 200 iterations produced a good set of candidate designs with favorable RMSD and i_pae scores.

When we increased the iterations to 500, the overall quality and success rate of the designs slightly decreased. Although the difference was not dramatic, it was contrary to our initial expectation that more extensive sampling would be beneficial.

This leads us to believe that there might be a "sweet spot" for the number of iterations that is specific to the design problem.

My Questions

1. Is this a commonly observed phenomenon? We hypothesize that beyond a certain point, a higher iteration count might not add significant value and could slightly increase the probability of exploring less productive regions of the conformational space.

2. Could you share any recommended strategies or heuristics for efficiently identifying this optimal iteration range for a new binder design project?

3. Are there any general principles regarding how factors like binder length or target surface complexity might influence this optimal range?

Thank you for your time and for sharing your expertise. Any insights you could provide would be immensely valuable.

No file, test, or entry point is named. Start by reading the documentation and configuration path for the diffuser.T iterations parameter, then compare the reported 200- and 500-iteration observations. Done would require a documented, evidence-based explanation or guidance on choosing iteration ranges for binder design.