Title: Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
Venue: NeurIPS 2025
Accepted: yes
Keywords: protein conformations

biomolecular systems

enhanced sampling

diffusion

importance sampling

protein flexibility
Abstract: The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or conformations. Researchers have endeavored for decades to develop computational methods to predict the distribution of conformations, which is far harder to determine experimentally than a static folded structure. We present ConforMix, an inference-time algorithm that enhances sampling of conformational distributions using a combination of classifier guidance, filtering, and free energy estimation. Our approach upgrades diffusion models---whether trained for static structure prediction or conformational generation---to enable more efficient discovery of conformational variability without requiring prior knowledge of major degrees of freedom. ConforMix is orthogonal to improvements in model pretraining and would benefit even a hypothetical model that perfectly reproduced the Boltzmann distribution. Remarkably, when applied to a diffusion model trained for static structure prediction, ConforMix captures structural changes including domain motion, cryptic pocket flexibility, and transporter cycling, while avoiding unphysical states. Case studies of biologically critical proteins demonstrate the scalability, accuracy, and utility of this method.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents ConforMix, a method for guiding diffusion-based biomolecular generative models (e.g., Boltz, AlphaFold3) to produce conformational ensembles—rather than single static structures—at inference time, without any model retraining or fine-tuning.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1.  The paper tackles a highly relevant and open challenge: predicting conformational ensembles of biomolecules, which has broad applications in structural biology and drug design.\n2.  The proposed method, ConforMix, is both novel and promising. Notably, it appears to outperform BioEmu—a state-of-the-art method trained on molecular dynamics data—in at least two respects: (i) recovery of native conformations under varying RMSD thresholds (Figures 2C and 2D), (ii) efficiency in free energy estimation (Section 4.1).\n3.  The proposed approach requires no retraining and could be widely applicable to pretrained diffusion models.\n\n**Weaknesses**\n\n1.  Experimental details are missing, making it hard to fully interpret the results:\n  - How were the proteins in Figures 2A and 2B selected? What exactly is being projected in the PCA plots?\n  - Are the protein sets in Figures 2C and 2D the same as those used in the BioEmu paper? Please explain the metrics.\n  - The conclusions from the RNA modeling experiment (starting line 230) are insufficiently supported. Are the grey structures in Figure 3B experimental references? Please support both the claim of lower performance on RNA than on proteins, as well as recapitulation of conformations from MDLu et al. 2025.\n2.  Although Section 3.3 discusses free energy prediction and sample reweighting, these components do not appear to be used or evaluated in the experiments. In Section 4.1, free energy estimation seems to be performed using the BioEmu method rather than the proposed one. A comparison is needed.\n3.  Runtime performance is not discussed.\n4.  Some parts lack clarity:\n  - TwistedSampling, central to ConforMix, is not described. A brief intuitive explanation in the main text and a detailed description in the appendix would be very helpful.\n  - [Line 7] Mentions classifier guidance, but it does not appear in the methods—this is also mentioned on line 113.\n  - [Line 149] It’s unclear what the variable j represents.\n  - [Lines 280–283] The discussion is confusing. Is the proposed method actually faster than existing ones?\n  - [Line 99] The conditioning variable is inconsistently denoted as both $input$ and $s$ later.\n5.  Typos and minor errors\n  - Line 78: “(,”\n  - Line 83: “[ cite …”\n  - Line 157: “[ CITE …”\n  - Figure 2: Floating letter “B” in the figure\n\nQUESTIONS:\n1.\tThe 10-residue sliding window for pLDDT (Section 3.2) is an interesting idea. Why was this chosen over, for example, simply averaging pLDDT over all residues?\n2.\tIn Algorithm 1, are samples with specific RMSD thresholds R guaranteed? For example, in Figure 2A, there appear to be no samples within 1Å RMSD for 13PK, despite this threshold being mentioned on line 190.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose ConforMix, a sampling method that employs Twisted Diffusion and Sequential Monte Carlo within a diffusion model. This method allows us to sample a wide variety of conformations, enabling us to capture the most important conformational changes for understanding biomolecular function.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThis is an interesting method of extending a diffusion model-based generative model, which essentially predicts static three-dimensional structures, to dynamic three-dimensional structures that can be predicted without information on degrees of freedom. This method uses sampling with Twisted Diffusion and Sequential Monte Carlo and reweighting with the Multi-State Bennett Acceptance Ratio (MBAR) free energy estimation algorithm to guide and reweight conditioning generation. When incorporated into Boltz, it can predict structures closer to those revealed by experiments that Boltz alone cannot predict. This method is more sophisticated and scalable than the previously reported AFCluster, and its performance is comparable to that of BioEmu.\n\nWeaknesses:\nIt seems that three-dimensional coordinate information is essential for input. The prediction accuracy is affected by the statistical uncertainty of MBAR, as mentioned in the limitations, and it may depend heavily on the accuracy of the extended diffusion model predictions. Therefore, validation experiments in a wet lab are ultimately required. However, this is not reason to lower the paper's rating, as this applies to all in silico models.\n\nMinor point: \nI found several typographical errors in the text. The following is a list of the ones we noticed:\nLine 78: Excessive parentheses.\nLines 83 and 157: The reference appears to be incomplete.\nFigure 3: \"applicaation\" is a typo for \"application.\"\nLine 235: “MDLu” is a typo for “MD Lu”.\nLine 293: “hte” is a typo for “the”.\n\nQUESTIONS:\nYou might be planned as the next step, but will it yield results for predicting protein complexes and extending docking simulations?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors developed ConforMix, an inference-time scaler for diffusion-based structure predictor of biomolecular systems, to enable multi-state sampling.\nUsing pseudo-potentials under different conditions and Twisted Diffusion Sampling (TDS), they were able to efficiently sample an extensive conformational space.\nBased on these results, the authors connected their method with the long-standing MBAR (multistate Bennett acceptance ratio) method, which is a free energy estimation method based on system perturbation. This allows for faster convergence of MBAR's energy estimation.\nThe authors chose important and quite different biomolecular systems to demonstrate ConforMix's multi-state sampling efficiency and adopted benchmark datasets to show its efficiency in free energy estimation.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n1. The proposed method uses system-dependent conditions with potentials for TDS and has demonstrated its ability to transform protein structure prediction, such as Boltzmann machines trained with static conformations, into multi-state conformational samplers.\n2. Fast free energy estimation, based on a combination of MBAR, seems to have the potential to be used in the analysis of protein dynamical systems.\n3. The motivation of the paper and the method and the experimental results are well aligned.\nThe paper's core result is that Boltz-ConforMix performs well in multi-state sampling, demonstrating its performance on domain and cryptic pocket motion datasets.\nTwo systems, which used for case studies, are quite different (SemiSWEET sugar transporter is a relatively less dynamic protein system where the hammerhead ribozyme is a more dynamic RNA system), demonstrating the proposed method's effectiveness.\n\n# Weakness\n1. **Lack of validations for generated conformations**. [related to **Q2**]  \nBoltz-ConforMix in Figure 2(c) shows that it beats the BioEmu's performance under the range around 3Å.\nHow can it be possible only using RMSD compared to the initially generated conformation from Boltz?\nGiven the fact that (a) BioEmu learned vast conformational space with dynamical data (b) Boltz only learned mostly stable data, I think it is still hard with ConforMix to generate physically validated structures while covering more extensive conformational space than BioEmu.\nThe authors should provide an analysis about the structural validity of Boltz-ConforMix.\n\n2. **Potential biased selection of structural visualzation**. [related to **Q4**]  \nHow did author selected the visualization results from data?\nSince several results are picked from low-density region of the PCA results, it seems that the authors chose the results with specific criteria, which should be mentioned in the manuscript.\nFor example, were structures chosen based on their distance from a cluster center, their energy, a specific structural motif, or another quantitative measure?\n\n3. **Incomplete manuscript**.\nAlthough the final quality of the paper does not depend on its initial completeness, most papers submitted to the conference are well-formatted, with complete citations and figures.\nHowever, this paper has several critical issues.\n- Incomplete citations (lines 83 and 157)\n- Line 157 reveals the authors' names, which violates the anonymity requirement\n- In Figure 2(b), the label under *hinge outward* structure seems to be fixed (0.46Å to 1DV2) -> (?Å to 1DV1)\n- In supplementary figures S3(c) and S3(d), it seems that the axes are swapped, making it difficult to understand whether BioEmu and ConforMix generated similar conformational distributions\n\n4. **Understanding the manuscript is quite hard due to the lack of information**.\n- RMSD metrics used for each domain motion and cryptic pocket sets seems to be global and local RMSDs (inferred from BioEmu). These information is critical for evaluating the results of the paper so the authors should give more information about each results' metrics.\n- In the algorithm section, RMSD first appears, but there is no explanation of why the mask is generated from the rigid element or what the mask does in RMSD. (A detailed explanation is provided in Section 4.1, especially in line 274.) Using captions for detailed explanations would help readers better understand.\n- The authors should explicitly inform the readers about related supplementary section. (e.g. supplement -> supplementary figure 4)\n\nQUESTIONS:\n# Questions\n1. I suggest using supplementary data 1 of [1], which consists of 92 fold-switching protein conformations. In [1], the authors tested the capability of AF-based models to sample multiple conformations of fold-switch proteins and measured the success rate of sampling multi-state conformations. Alternatively, the dataset used in EigenFold (originally constructed from [3]) can be used for benchmarking. This benchmark implicitly evaluates the models' ability to sample multiple states using TM-score and residue-wise flexibility as metrics. Conducting such benchmarks would improve the paper's impact.\n2. Boltz only trained to reproduce the static (mostly stable) PDB structures, not the physical energy function $-\\nabla U(x)$. Thus, the drift that Boltz learned is not a physical force. It seems that using a pseudo-potential like RMSD might lead to highly physically invalid structures. I think that the authors' choice to use a pLDDT value higher than 20% for filtering generated structures implies that most of generated samples are in a physically invalid (off-manifold) state. Could the authors provide the statistics on physical validity of ConforMix-generated samples compared to Vanilla-Boltz-generated samples?\n3. The accuracy of MBAR estimation depends on the number of samples from each state and the overlap between states. To me, efficient sampling of various states and MBAR accuracy seem to be mutually exclusive. Is there any potential bias triggered by a small number of samples due to efficient exploration?\n4. The dynamic state visualization results of ConforMix presented in the main text and supplementary data are very impressive. By what criteria were these examples selected among the numerous structural ensembles generated?\n5. I think multi-state conformation sampling is more important for novel proteins rather than the systems that we know about.\nIs this right?\nIf so, how can one use ConforMix to fully novel protein, where no information about multiple conformational states are available?\nUser may select the suitable pseudo-potential with appropriately consider physical validity, and then should properly select the generated conformations.\nIf the authors provide proper ways to design pseudo-potential and conformer selection criteria (which is related to **W2** and **Q4**), this would enlarge the impact of the work.\n\n# Typos\n1. In Figure 3 caption's 4'th line: applicaation -> application\n2. In line 276: an series -> a series\n3. In line 293: What does mean by hte?\n4. The year is missing from reference 4.\n\n[1] Chakravarty, Devlina, et al. \"AlphaFold predictions of fold-switched conformations are driven by structure memorization.\" Nature communications 15.1 (2024): 7296.  \n[2] Jing, Bowen, et al. \"Eigenfold: Generative protein structure prediction with diffusion models.\" arXiv preprint arXiv:2304.02198 (2023).   \n[3] Chakravarty, Devlina, and Lauren L. Porter. \"AlphaFold2 fails to predict protein fold switching.\" Protein Science 31.6 (2022): e4353.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes the algorithm ConforMix for sampling from a distribution of conformations. ConforMix requires a pretrained generative model as its backbone and aims to improve the prediction of conformations produced by this model. ConforMix combines three separate algortihmic components:\n- To reweight samples, the partition function of the imposed condition is estimated via the multistate Bennett acceptance ratio. \n- Twisted Diffusion is applied to condition the sampling process on any differentiable feature.\n- The generated samples of ConforMix are rejected if any 10-residue sliding window exhibits an average pLDDT value more than 20% lower than that of the default prediction.\n\nConforMix is implemented in Boltz to generate diverse conformations rather than a single conformation for a given input, and in BioEmu to improve free energy estimation. The authors commit to releasing these implementations upon publication, and code snippets are already provided in the supplementary material.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- ConforMix is implemented in the open-source tools Boltz (an open-source, diffusion-based structure prediction model) and BioEmu (a diffusion-based model for conformational sampling). The authors commit to releasing these implementations upon publication, enabling future research to easily test ConforMix and integrate it with their own conformation-generating models.\n- ConforMix empirically broadens the accessible states of proteins, as demonstrated in four protein studies using Boltz. When combined with BioEmu, ConforMix can generate ensembles that extend beyond those sampled with BioEmu alone. In addition, applying ConforMix to RNA structures shows that it can recapitulate transitions observed in molecular dynamics simulations.\n- The advantage of integrating ConforMix is demonstrated for both a static conformation-generating model (Boltz) and a dynamic one (BioEmu).\n\n### Weaknesses\n- The paper does not introduce new methodologies but rather combines existing methods into an ensemble tailored to improve the prediction of conformations generated by a given model, whether static or dynamic. Nonetheless, the reviewer acknowledges the careful application of these methods, which leads to practical improvements.\n\nQUESTIONS:\n- What is the computational cost of ConforMix? How long is the sampling time of ConforMix+Boltz compared to sampling with Boltz only? How long is the sampling time of ConforMix+BioEmu compared to BioEmu only?\n- Is it necessary to estimate the normalizing constants  $Z_{1},...,Z_{J}$ appearing in line 173, as well as in Eq. (3) and Eq. (4), in order to solve the linear system described in lines 178–181? If so, how do the authors estimate these normalizing constants? \n- How large is $J$ in the experiments of the paper? Is it clear that the system of $J+1$ unknowns admits a (unique) solution?\n\n### Minor comments:\n- To open up the content of the paper to people with a computer science background only, it would help to add references to the first paragraph of the introduction. The second paragraph of the introduction is missing in the reviewers opinion some references as well.\n- there are also no references in the second paragraph of the introduction\n- (Line 83): internal comment \n- (line 92): broken reference\n- (line 151): bracket and period are interchanged.\n- (Line 157): internal comment \n- (line 235): is \"MDLu et al.\" correct?\n- (line 292): \"the\" instead of \"hte\"\n\nLIMITATIONS:\nThe authors have sufficiently addressed the limitations of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}