Further Response to Reviewer gNH7
**Comment 6**: What is the efficiency of calculating different interaction types?
**Response 6**: Thanks for the question! In Figure 7 included in the Appendix, we compared the average generation time of PocketFlow and its variants such as PocketFlow without different guidance terms. We can observe that calculating the protein-ligand interaction for guidance will not bring much overhead (~28% of the total generation time).
To detect the different interaction types in the generated pockets, we leverage PLIP and posecheck. They are also efficient tools and it totally takes around 20 seconds to process 100 generated pockets.
**Comment 7**: Is there any metric to evaluate the correctness of the interactions generated?
**Response 7**: Thanks for the question! In PocketFlow, we used the protein-ligand interaction profiler (PLIP) [69] to detect and annotate the protein-ligand interactions for each residue by analyzing their binding structure (Table 3 and lines 304-314 of the paper). Generally, PLIP is based on physical/chemical rules and employs four steps including structure preparation, functional characterization, rule-based matching, and filtering to detect the generated interactions. In the PLIP paper [69], the authors compared the detected/ground truth interactions of 30 literature-validated examples and achieved good consistency. Therefore, we can regard the detected interactions by PLIP as correct.
**Comment 8**: The original IPA relies on the rotational equivariance of frame orientation to achieve model’s invariance. However, the proposed method additionally treats the ligand atom as a residue and uses an invariant identity matrix to represent its orientation. Will the proposed IPA still output invariant embeddings?
**Response 8**: Thanks for the insightful question! We agree that IPA relies on the rotational equivariance of frame orientation to achieve the model’s invariance. In our case, there is no canonical orientation for the ligand atoms and we set them as an identity matrix for simplicity.
To achieve invariant embeddings, we can initialize the protein scaffold by aligning the protein principal axes with the coordinate axes. This can be achieved by subtracting the center of mass (COM), computing the inertia matrix, diagonalizing the inertia matrix, and aligning the protein to principal axes. In experiments below, we compared such an initialization strategy with the default setting (only subtracting COM). The experimental results are comparable and show no significant differences. Therefore, we use the default setting for simplicity. We will include more discussions in our revised paper.
| Model | CrossDocked | | | Binding MOAD | | |
|---------------|-----------------------|-------------------|------------------|----------------------|-------------------|------------------|
| | AAR (↑) | scRMSD (↓) | Vina (↓) | AAR (↑) | scRMSD (↓) | Vina (↓) |
| PocketFlow | 52.19±1.34% | 0.67±0.04 | -8.236±0.16 | 54.30±1.70% | 0.68±0.03 | -9.370±0.24 |
| w/ principal axes aligning | 52.12±1.29% | 0.67±0.03 | -8.227±0.18 | 54.47±1.73% | 0.69±0.03 | -9.362±0.28 |
**Comment 9**: How are the guidance coefficients for different guidance mechanisms determined?
**Response 9**: In the default setting, the guidance coefficients of PocketFlow including $\gamma, \xi_1, \xi_2,$ and $\xi_3$ are set as 1 and achieve good results. We also explore the influence of guidance coefficients in the appendix. For example, in Figure. 8 of the submitted paper, we explore the impact of Affinity Guidance Strength ($\gamma$) on various generation metrics. As $\gamma$ is scaled up, the Vina Score significantly improves and quickly stabilizes; AAR initially increases before gradually decreasing; scRMSD, on the other hand, increases with higher $\gamma$. These observations underscore the importance of selecting an appropriate $\gamma$ to effectively balance the guidance and unconditional terms. While Affinity Guidance promotes the generation of high-affinity pockets, an excessively high $\gamma$ can result in less valid pocket sequences or structures. In the default configuration, $\gamma$ is set to 1 for simplicity.
**Comment 10**: Is RFDiffusionAA trained on the same dataset as the proposed model?
**Response 10**: As indicated in lines 822-823, we used the provided checkpoints of RFDiffusionAA for all the experiments because the training code is not available (https://github.com/baker-laboratory/rf_diffusion_all_atom). The original training data of RFDiffusionAA contains 121,800 protein-small molecule structures, 112,546 protein-metal complexes, and 12,689 structures with covalently modified amino acids, which represent a broad set of protein-ligand complex structures.