Protein Folding MAE

1. Brief critique and context: The conjecture focuses on the challenge of predicting side-chain conformations in protein folding, a critical aspect for accurate molecular modeling. Traditional methods often rely on energy-based refinement to improve accuracy, and achieving a mean absolute error (MAE) below 0.5 Å for all amino acids without such refinement is considered difficult. This sets a high bar, reflecting the complex interplay of forces and sterics in proteins.

2. Recent research: Recent advances in AI-driven algorithms, like AlphaFold by DeepMind, have significantly improved protein structure predictions (https://www.nature.com/articles/s41586-021-03819-2). However, these models still rely on energy-based post-processing to refine side-chain conformations. Newer models like RosettaFold (https://www.science.org/doi/10.1126/science.abj8754) show promise but also incorporate energy-based refinement in their pipeline.

3. Bayesian likelihood of falsification: 30%. While deep learning models have rapidly advanced in accuracy and capability, the precise prediction of side-chain conformations without energy refinement remains a significant hurdle. Given the current trajectory of research, it's plausible that breakthroughs could occur, but the specific constraints of the conjecture make it less likely to be fully falsified within five years.