- The prediction process is faster, but final performance significantly decreases.
- Removing IPA is disadvantageous, as the structure module is less costly than Evoformer.
- Kernels are implemented with OpenAI's Triton, not CUDA; a full-page explanation is unnecessary due to well-known engineering improvements.
- The analysis of kernels is wrong. For example, "This reduces the number of reads from 5 to 1, and the number of writes from 4 to 1". The Wx and Vx are matrix multiplication operators, which will call GEMM kernels, thus these read/write cannot be merged. We usually can only save the read/write times for element-wise operators.
- The method relies on a computationally demanding pretrained protein language model; simplification would be beneficial.
- Coordinate recovery omits chirality consideration, potentially negatively impacting performance.
- In-depth analysis of uncertainty estimation technique is needed for better understanding of robustness.
- Kernels are implemented with OpenAI's Triton, not CUDA; a full-page explanation is unnecessary due to well-known engineering improvements.