Title: DynaPhArM: Adaptive and Physics-Constrained Modeling for Target-Drug Complexes with Drug-Specific Adaptations
Venue: NeurIPS 2025
Accepted: yes
Keywords: Target-drug complex dynamic modeling

Physics constraints

Denoising diffusion probabilistic model

Flexible docking
Abstract: Accurately modeling the target-drug complex at atom level presents a significant challenge in the computer-aided drug design. Traditional methods that rely solely on rigid transformations often fail to capture the adaptive interactions between targets and drugs, particularly during substantial conformational changes in targets upon ligand binding, which becomes especially critical when learning target-drug interactions in drug design. Accurately modeling these changes is crucial for understanding target-drug interactions and improving drug efficacy. To address these challenges, we introduce DynaPhArM, an SE(3)-Equivariant Transformer model specifically designed to capture adaptive alterations occurring within target-drug interactions. DynaPhArM utilizes the cooperative scalar-vector representation, drug-specific embeddings, and a diffusion process to effectively model the evolving dynamics of interactions between targets and drugs. Furthermore, we integrate physical information and energetic principles that maintain essential geometric constraints, such as bond lengths, bond angles, van der Waals forces (vdW), within a multi-task learning (MTL) framework to enhance accuracy. Experimental results demonstrate that DynaPhArM achieves state-of-the-art performance with an overall root mean square deviation (RMSD) of 2.01 Å and a sc-RMSD of 0.29 Å while exhibiting higher success rates compared to existing methodologies. Additionally, DynaPhArM shows promise in enhancing drug specificity, thereby simulating how targets adapt to various drugs through precise modeling of atomic-level interactions and conformational flexibility.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents DynaPhArM, an SE(3)-Equivariant Transformer framework that dynamically models target-drug interactions with physics-based constraints. By integrating a cooperative scalar-vector encoding, a discretization module, physics-aware cross-attention, and a diffusion process, the model captures atomic-level flexibility and conformational dynamics in protein–ligand binding. Empirical evaluations demonstrate state-of-the-art performance in complex reconstruction, cross-docking, and binding affinity prediction.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths** \n1. The model integrates physical constraints and structural knowledge, with clearly motivated module design.\n2. The paper is well-organized, and the appendices are comprehensive.\n3. It significantly enhances the flexible modeling of protein–drug complexes and achieves state-of-the-art results across multiple downstream tasks.\n\n**Weaknesses**\n1. The discussion in the introduction regarding the pros and cons of Transformers and GNNs lacks clear connection to the paper’s motivation.\n2. Figure 5's annotation is confusing, reducing clarity of the visual results. For example, in line 726, it is unclear what the 40% refers to, and whether the PoseBusters (PB) success rate overlaps with the overall success rate.\n3. The detailed dataset descriptions in Section 4.1 could be moved to the appendix to make space for more experimental results, such as the ablation studies currently in Appendix I.\n\n*Typo*\n\nLine 719: “Te” → “The”.\n\nQUESTIONS:\n1. What do the variables $L_b$, $L_s$, and $d_e$ in line 154 represent? \n2. How much computational overhead is introduced by the Lennard-Jones potential term? Is there profiling or ablation study on its effect?\n3. Why does the paper not include a comparison with AlphaFold 3 on the PoseBusters benchmark, given AlphaFold 3 is a strong baseline?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces DynaPhArM, a physical constrained diffusion model for accurately modeling target-drug complexes at the atomic level, addressing the limitations of traditional rigid transformation methods that fail to capture dynamic conformational changes. DynaPhArM proposes a cooperative scalar-vector representation, drug-specific embeddings, and a diffusion process to model evolving target-drug interactions, while integrating physical constraints within a multi-task learning framework to ensure geometric and energetic plausibility. Experimental results demonstrate state-of-the-art performance in complex structure reconstruction.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n1. The method is well motivated.\n\n2. The paper is well structured, and each module is provided with sufficient motivation and detailed explanations.\n\n3. The experiments are comprehensive.\n\n4. Open-source code is provided to facilitate the reproduction of the paper's results.\n\n**Weakness**\n\n1. In the interaction module, the magnitude of $\\gamma_i$ may cause the gradient to be dominated by the physical matrix, leading the model to overfit priors and ignore novel interaction patterns. How do DynaPhArM ensures that the learned $\\gamma_i$ is the optimal result?\n\n2. DynaPhArM relies on a physical-constrained interaction matrix calculated via LJ potential, which may affect the generalizability. For example, when encountering new binding modes not previously modeled by the model which is used to calculate the LJ potential, DynaPhArM might fail to accurately describe interactions, causing attention to deviate from true binding sites.\n\n3. Compared to methods like DynamicBind, DynaPhArM does not introduce conformational entropy terms or diversity rewards in the loss function, potentially biasing generated structures toward common modes. How is the diversity issue addressed is DynaPhArM?\n\n4. In the cooperative scalar-vector representation algorithm, what is the setting of the spherical harmonic expansion order $L$ (Appendix D - Algorithm 2 - line 19), and how does this parameter setting impact the model?\n\n5. I suggest that authors can provide specific metrics such as FLOPs and model parameter counts to better present the model complexity.\n\nQUESTIONS:\nSee Weakness\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DynaPhArM, an SE(3)-equivariant Transformer model designed for flexible protein-ligand docking. The method aims to capture the dynamic adaptations of a target protein upon binding to different drugs, often referred to as induced fit. Key contributions include a cooperative scalar-vector representation module, a physics-constrained interaction module that incorporates Lennard-Jones potentials as an attention bias, and a diffusion denoising process to model conformational flexibility. The model is trained end-to-end with a multi-task loss function that enforces both structural accuracy (compared to crystal structures) and physical plausibility. The authors demonstrate that DynaPhArM achieves state-of-the-art performance on several benchmarks for complex structure reconstruction, cross-docking, and binding affinity prediction.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. Novel and Comprehensive Architecture: The proposed architecture is sophisticated and well-motivated. Integrating an SE(3)-equivariant framework, a diffusion model for flexibility, and physics-based priors into a single end-to-end model is a non-trivial and powerful approach for flexible docking.\n\n2. Strong Empirical Results: The model demonstrates impressive performance across a wide range of tasks and challenging datasets. Achieving top results in both structure prediction (low RMSD) and downstream binding affinity prediction (high PCC) suggests that the generated conformations are not only geometrically accurate but also biochemically meaningful.\n\n3. Emphasis on Physical Plausibility: The inclusion of a detailed physics and geometry-guided loss function is a significant strength. This addresses a common pitfall of deep learning models, ensuring that the generated structures adhere to fundamental chemical principles, which is critical for real-world drug design applications.\n\n**Weaknesses**\n1. Clarity of Model Description: The description of key model components, particularly the \"Cooperative scalar-vector representation\" (Section 3.1), lacks clarity. The text describes a complex update mechanism without accompanying formulas in the main text, making it difficult for readers to fully grasp the implementation details.\n\n2. Potentially Overstated Claims on \"Dynamics\": The paper claims to model \"dynamic alterations\" and \"evolving dynamics.\" However, the \"dynamic\" information seems to be primarily derived from static structural data (PDB) and static physics priors (Lennard-Jones potentials from AutoDock). This is fundamentally different from simulating a physical trajectory (e.g., via MD). The term \"dynamic\" could be misleading and warrants more precise framing.\n\n3. Ambiguity in the Diffusion Process and Flexibility Claims: The description of the diffusion process is confusing. The authors state their process does not sample from a Gaussian prior (line 173) but later use DDPM, which traditionally involves Gaussian noise. This needs clarification. Furthermore, the claim that the generated \"conformational flexibility\" is a reflection of dynamics is questionable, as it is learned from a collection of static snapshots rather than true temporal data, and the generated latents are 'pseudo-flexibility'.\n\n4. Insufficient Detail on the Training Dataset: As a data-driven method, the composition of the training set is paramount. The paper provides only the total number of complexes (21,762) but lacks crucial statistical details. For the model to genuinely learn \"drug-specific adaptations,\" the dataset should contain a sufficient number of targets with multiple, diverse ligand-bound structures. The lack of this information makes it hard to assess whether the model's capabilities stem from a truly diverse dataset or from memorizing patterns in a few well-represented protein families, e.g., kinases.\n\nQUESTIONS:\nI have a few key questions and suggestions for the authors. A clear response could significantly strengthen the paper and potentially raise my evaluation score.\n\n1. Clarification of the Cooperative Scalar-Vector Representation: The description of this module in Section 3.1 (lines 114-131) is quite dense and lacks formal equations. Could the authors please provide a more structured description, perhaps with key formulas (similar to those in Section 3.2) ? Specifically, how are the directional messages $h_{ij}$, attention weights $\\alpha_{ij}$, and cross-modal message hinteract formally defined and integrated? This would greatly improve the reproducibility and clarity of this core component.\n\n2. Reframing the \"Dynamic\" Nature of the Model: I appreciate the goal of modeling induced fit, but the term \"dynamic\" may be an overstatement. The model learns from static snapshots and uses static force field rules.  A more precise explanation of how the model generalizes from a set of static structures to predict adaptations for novel ligands would be very insightful. The core question is: is the model truly simulating a process, or is it performing pattern recognition to predict a likely end-state conformation?\n\n3. Elucidation of the Diffusion Process and Flexibility: On line 173, you state the diffusion process starts from the initial joint embedding e_f \"Instead of sampling from a Gaussian prior.\" This is confusing. The forward process of a DDPM typically adds Gaussian noise to reach a standard normal distribution $N(0, I)$. The reverse process then learns to denoise from this distribution. Please clarify:\n\n(a) What is the exact nature of the noise distribution at step T? If not N(0, I), what is it and why?\n\n(b) How does this specific diffusion process better \"reflect conformational flexibility\" compared to a standard DDPM? The flexibility learned here appears to be a statistical interpolation between different static states in the training data. Could you elaborate on why this should be considered a robust representation of physical flexibility, especially when compared to methods that use, for example, MD simulation data or normal mode analysis?\n\n4. Providing More Training Dataset Statistics: The performance of a data-driven model is critically dependent on its training data. Could you please provide more detailed statistics about the curated training set of 21,762 complexes? Key information would include:\n\n- The distribution of the number of unique ligands per protein target.\n\n- The number of protein targets that have, for example, >5, >10, or >20 different co-crystal structures in the dataset.\n\n- A breakdown of the protein families represented.\n\nThis information is crucial for the community to understand the scope and potential biases of the training data and to fairly assess the model's ability to generalize to targets with sparse structural information. A positive response with these details would significantly increase my confidence in the model's claimed ability to capture drug-specific adaptations.\n\nLIMITATIONS:\nYes, the authors have included a dedicated limitations section in Appendix L, which is commendable. They discuss dependency on input structure quality, the computational overhead of the diffusion module, and assumptions about task importance. The discussion is transparent and constructive. No further suggestions on this point.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DynaPhArM, a SE(3)-equivariant Transformer-based framework for modeling target–drug complexes with explicit consideration of protein conformational flexibility. By combining a cooperative scalar–vector representation, a diffusion-based dynamic embedding module, and multi-task physical–geometric constraints, DynaPhArM aims to generate physically realistic and chemically plausible 3D structures of protein–ligand complexes. Experiments across multiple benchmarks demonstrate that DynaPhArM achieves strong results compared to several classical and diffusion-based baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The cooperative scalar–vector encoding scheme enables tightly coupled geometric and semantic representations than standard SE(3) transformers.\n2. Incorporating a physics-constrained multi-task loss with seven biophysically inspired regularizations provides a strong inductive bias toward realistic molecular structures.\n3. The experimental evaluation is thorough, including diverse datasets and ablation studies, demonstrating consistently good performance in RMSD and binding affinity prediction.\n\n**Weaknesses**\n\n1. The paper does not include comparisons with recent baseline methods, such as Re-Dock [1], which are highly relevant benchmarks.\n2. The approach relies on pretrained codebooks (FoldToken4) and AutoDock-generated interaction priors, which might introduce bias or performance bottlenecks if these priors are imperfect.\n3. The reproducibility could be improved by releasing more detailed hyperparameters details.\n\n[1] Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge, ICML 2024\n\nQUESTIONS:\n1. Could the authors clarify how DynaPhArM would perform compared to Re-Dock?\n2. How sensitive is DynaPhArM to potential errors in the AutoDock priors, and what strategies might mitigate their impact?\n\nLIMITATIONS:\nThe authors provide a discussion of the limitations of their work, including the reliance on high-quality structural inputs, the computational cost of the diffusion module, and the equal-task-weight assumption in the multi-task loss. These points are described in Appendix L, with suggestions for future research directions. I appreciate this candid reflection, which demonstrates the authors' awareness of practical deployment challenges.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}