PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking

• URL: http://arxiv.org/abs/2505.01700v2
• Date: Wed, 21 May 2025 06:15:35 GMT
• Title: PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
• Authors: Yize Jiang, Xinze Li, Yuanyuan Zhang, Jin Han, Youjun Xu, Ayush Pandit, Zaixi Zhang, Mengdi Wang, Mengyang Wang, Chong Liu, Guang Yang, Yejin Choi, Wu-Jun Li, Tianfan Fu, Fang Wu, Junhong Liu,
• Abstract summary: PoseX is an open-source benchmark to evaluate both self-docking and cross-docking.<n>We incorporated 23 docking methods in three methodological categories.<n>We developed a relaxation method for post-processing to minimize conformational energy and refine binding poses.
• Score: 74.76447568426276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks requiring extensive training, posing challenges for convenient and efficient assessment of docking methods. To fill these gaps, we design PoseX, an open-source benchmark to evaluate both self-docking and cross-docking, enabling a practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self-docking and 1,312 entries for cross-docking; second, we incorporated 23 docking methods in three methodological categories, including physics-based methods (e.g., Schr\"odinger Glide), AI docking methods (e.g., DiffDock) and AI co-folding methods (e.g., AlphaFold3); third, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourth, we built a leaderboard to rank submitted models in real-time. We derived some key insights and conclusions from extensive experiments: (1) AI approaches have consistently outperformed physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics-based post-processing could achieve excellent performance. (3) AI co-folding methods exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting modeling on stereochemistry improves the structural plausibility markedly. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for AI co-folding methods, in future modeling efforts. The code, dataset, and leaderboard are released at https://github.com/CataAI/PoseX.

Related papers

• Enhancing Training Data Attribution with Representational Optimization [57.61977909113113]
  Training data attribution methods aim to measure how training data impacts a model's predictions.<n>We propose AirRep, a representation-based approach that closes this gap by learning task-specific and model-aligned representations explicitly for TDA.<n>AirRep introduces two key innovations: a trainable encoder tuned for attribution quality, and an attention-based pooling mechanism that enables accurate estimation of group-wise influence.
  arXiv  Detail & Related papers  (2025-05-24T05:17:53Z)
• Fast and Accurate Blind Flexible Docking [79.88520988144442]
  Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets plays a vital role in drug discovery.<n>We propose FABFlex, a fast and accurate regression-based multi-task learning model designed for realistic blind flexible docking scenarios.
  arXiv  Detail & Related papers  (2025-02-20T07:31:13Z)
• DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking [15.205550571902366]
  Molecular docking is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. We propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking.
  arXiv  Detail & Related papers  (2024-10-15T03:09:06Z)
• One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning [6.605588716386855]
  We show that LigPose can be accurately tackled with a single model, namely LigPose, based on multi-task geometric deep learning. LigPose represents the ligand and the protein pair as a graph, with the learning of binding strength and atomic interactions as auxiliary tasks. Experiments show LigPose achieved state-of-the-art performance on major tasks in drug research.
  arXiv  Detail & Related papers  (2024-08-21T05:53:50Z)
• Deep Learning for Protein-Ligand Docking: Are We There Yet? [5.721438704473567]
  We introduce PoseBench, the first comprehensive benchmark for broadly applicable protein-ligand docking.<n>PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein-ligand docking and protein-ligand structure prediction.
  arXiv  Detail & Related papers  (2024-05-23T02:27:39Z)
• Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge [69.80471117520719]
  Re-Dock is a novel diffusion bridge generative model extended to geometric manifold. We propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations. Experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
  arXiv  Detail & Related papers  (2024-02-18T05:04:50Z)
• Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking [17.28573902701018]
  Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets. We introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking.
  arXiv  Detail & Related papers  (2023-11-30T14:09:20Z)
• FABind: Fast and Accurate Protein-Ligand Binding [127.7790493202716]
  $mathbfFABind$ is an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. Our proposed model demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods.
  arXiv  Detail & Related papers  (2023-10-10T16:39:47Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• Re-Evaluating LiDAR Scene Flow for Autonomous Driving [80.37947791534985]
  Popular benchmarks for self-supervised LiDAR scene flow have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sampling patterns. We evaluate a suite of top methods on a suite of real-world datasets. We show that despite the emphasis placed on learning, most performance gains are caused by pre- and post-processing steps.
  arXiv  Detail & Related papers  (2023-04-04T22:45:50Z)
• Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics [77.34726150561087]
  Recent developments in artificial neural networks, particularly deep learning (DL), are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics.
  arXiv  Detail & Related papers  (2022-12-18T02:03:00Z)
• Efficient Few-Shot Object Detection via Knowledge Inheritance [62.36414544915032]
  Few-shot object detection (FSOD) aims at learning a generic detector that can adapt to unseen tasks with scarce training samples. We present an efficient pretrain-transfer framework (PTF) baseline with no computational increment. We also propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights.
  arXiv  Detail & Related papers  (2022-03-23T06:24:31Z)
• DOCKSTRING: easy molecular docking yields better benchmarks for ligand design [3.848364262836075]
  We present DOCKSTRING, a bundle for meaningful and robust comparison of machine learning models consisting of three components. The Python package implements a robust ligand and target preparation protocol that allows non-experts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix.
  arXiv  Detail & Related papers  (2021-10-29T01:37:13Z)
• PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics [89.81550748680245]
  We introduce a new differentiable physics benchmark called PasticineLab. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
  arXiv  Detail & Related papers  (2021-04-07T17:59:23Z)
• PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification [55.887502438160304]
  The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. We improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.
  arXiv  Detail & Related papers  (2020-02-09T04:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.