Kati8/ICLR_2023_2025 huggingface dataset:
authors: Gabriele Corso
authors: Vignesh Ram Somnath
authors: Noah Getz
authors: Regina Barzilay
authors: Andreas Krause
reviews: 
Comments: This paper proposes Unbalance Flow Matching and demonstrates its application to flexible docking, and enhances its performance from 30% to 73%. For those who have attempted the traditional flow on flexible docking or protein domain structural changes, they must have encoutered the same problem addresses in this paper. It offers a good solution backed by theoretical insights and supporting proofs.
Strengths: - It proposes the Unbalance Flow Matching (UFM), which extends the traditional flow matching with the coupling q by performing some filtering (e.g., RMSD) in practice.
- The proposed two-stage flow matching process, consisting of a rough docking stage called “manifold docking” and a more precise “relaxation” stage, effectively boosts the PB valid benchmark to 73% while maintaining reasonable computational costs.
Weaknesses: - I do not see major weakness in this paper, but have several questions as follows.
Recommendation: 10
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This papers introduces Unbalanced Flow Matching (UFM), an extension of traditional flow matching, particularly tackling molecular docking problems considering protein flexibility. While traditional molecular docking methods assumed rigid structure for proteins limiting the accuracy, sometimes failing in scenarios including conformational changes of protein. Based on UFM, the authors propose the framework FLEXDOCK that considers flexible docking, and structure relaxation for optimized pose.
Strengths: 1. A new paradigm coined Unbalanced Flow Matching

The proposed objective is an extension of the traditional flow matching. Relaxing the strict marginal conservation allows more efficient and accurate mapping between distributions.

2. Two subtasks for flexible docking

By dividing flexible docking into two subtasks, manifold docking, and structure relaxation, the method results in more physically realistic structures.

3. Empirical results

FLEXDOCK outperforms prior methods on the PDBBind benchmark. Noticeably, this has been done using the same training data and model architecture as DiffDock-Pocket. Ablation studies also verify each component of the proposed method.
Weaknesses: 1. Flexibility and complexity tradeoff

While UFM has strength in flexibility compared to prior works, it inevitably seems to face more complexity than before. Could the authors briefly summarize the complexity compared to the algorithms in the prior works, if possible?

2. Choice of coupling

UFM seems to rely much on the coupling distribution. The authors have empirically chosen cutoffs, but this dependency may have problems for generalization. I may have misunderstood the part of choosing the coupling.
Recommendation: 8
Reviewer confidence: 2
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: The authors propose FlexDock, a flow matching-based method for flexible small molecule docking. To address the problem of the limited availability of the bound protein structures, authors formulate Uncoupled Flow Matching, in which they tradeoff the approximation quality of the target distribution and the amount of samples needed from the prior distribution. Authors show that FlexDock outperforms other flexible docking methods in various metrics.
Strengths: 1. The proposed Unbalanced FM framework is a very neat idea and can be very useful in many different applications where the data from one of the distributions is limited
2. FlexDock demonstrates impressive performance across multiple metrics
Weaknesses: 1. In the motivation for the unbalanced flow matching authors say that due to the limited availability of the data points from $q_1$ (i.e. holo structures), the expected length of the flow will be large and the expected OT cost between $q_0$ and $q_1$ will be high. In my opinion, that would be true if there was a certain level of diversity in samples from $q_0$ (i.e. different apo structures) that correspond to a single (available) sample from $q_1$. But is it really the case for protein structures? I have a feeling that the diversity in apo structures of the same protein is not that high. I believe that the proposed method indeed improves learning the transport, as demonstrated in Figure 4. But I think it would be very beneficial to dissect this experiment and look at the flow distances and transport costs for different cutoffs, or even just plot these distributions for your training data. In my opinion, such a deeper analysis will strengthen the motivation a lot.

2. Authors suggest to use the coupling of the form $q(x0,x1) = q_0(x_0)q_1(x_1)\mathbb{I}_{c(x_0, x_1)<cdock}$. 
* 2.1 Does it mean that some examples where conformation change upon binding is significant are completely discarded from the training?
* 2.2 If 2.1 is true, how does the model perform on similar examples (i.e. the ones that change a lot upon binding) from the test set?
* 2.3 Can it be true that "All-Atom RMSD% < 1A = 41.7%" means that around 40% of the test set are proteins that almost don't change upon binding, and the model (trained on couplings with rather strict $c_{dock}$) just doesn't change these structures meaning that it "recovers" holo conformations for these 40% correctly?


**Minor comments:**
1. Lines 40-45: could you please provide the references to the works you’re discussing here?
2. Are the protein states over the trajectory physically-realistic?
3. Could you please provide a more detailed description of y-axis in Figure 4 (include "% of passed predictions" or something like this)?
4. Line 273 - 274 -> I could not find the introduction of the coupling $q(x0, x1) = q(x0)q(x1)\mathbb{I}$ in the previous section. What are you referring to?
5. Line 182 – I am not sure if it is a typo or maybe I am wrong, but to me it looks like (3) is the lower bound of (2).
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper extends Unbalanced Flow Matching (UFM), a novel approach to molecular docking that extends Flow Matching (FM) to better handle protein flexibility and generate physically realistic poses. The main innovation is allowing trade-offs between sample efficiency and approximation accuracy in the transport between distributions. The authors implement this in FLEXDOCK, which chains two UFM components: manifold docking for approximate pose prediction and structure relaxation for refinement. On the PDBBind benchmark, FLEXDOCK improves both docking accuracy and the proportion of energetically favorable poses compared to existing methods.
Strengths: 1. This paper attempts to address two critical limitations found in previous docking methods: the rigid assumption and the generation of non-physical structures.
2. The extensive experiments demonstrate the effectiveness of the proposed method. This paper demonstrates significant improvement in the PB-Valid metric, particularly with the simple yet effective energy loss.
3. The paper analyzes and formulates the flexible docking problem from the perspective of distribution transport, and extends the Flow Matching method specifically for this problem, supported by theoretical derivations.
Weaknesses: The proposed method and experiments still assume prior knowledge of the binding pocket; despite this known pocket assumption, FLEXDOCK shows no significant improvement in RMSD-related metrics. This suggests that the manifold docking module may not make substantial progress in resolving the approximate pose compared to previous works.
However, this limitation appears to be inherent to the challenging nature of the protein-ligand docking problem and reflects the current state of the field, rather than diminishing the paper's existing contributions.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This work introduces Unbalance Flow Matching and demonstrates its utility in the context of flexible molecular docking, achieving a substantial performance improvement from 30% to 73%. Researchers working with conventional flow-based methods on flexible docking problems or modeling conformational changes in protein domains will recognize the challenge this paper addresses. The contribution is supported by solid theoretical foundations and rigorous mathematical justification.
Strengths: - The introduction of Unbalance Flow Matching (UFM) represents an important advancement, extending standard flow matching through incorporation of a coupling mechanism q and application of practical filtering strategies such as RMSD-based selection.
- The dual-phase approach combining an initial "manifold docking" stage for coarse alignment followed by a "relaxation" stage for fine-tuning refinement yields impressive results on the PB valid benchmark (73%) while maintaining computational efficiency.
Weaknesses: - The paper does not present significant limitations, though several clarifying questions arise from the work.
Recommendation: 10
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 3