3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery

• URL: http://arxiv.org/abs/2502.05107v1
• Date: Fri, 07 Feb 2025 17:28:10 GMT
• Title: 3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery
• Abstract summary: 3DMolFormer is a unified dual-channel transformer-based framework applicable to both docking and 3D drug design tasks.<n>We represent 3D pocket-ligand complexes using parallel sequences of discrete tokens and continuous numbers, and we design a corresponding dual-channel transformer model to handle this format.<n> Experimental results demonstrate that 3DMolFormer outperforms previous approaches in both protein-ligand docking and pocket-aware 3D drug design.
• Score: 17.78777622199143
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Structure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challenge in drug discovery. However, no existing work can deal with both tasks to effectively leverage the duality between them, and current methods for each task are hindered by challenges in modeling 3D information and the limitations of available data. To address these issues, we propose 3DMolFormer, a unified dual-channel transformer-based framework applicable to both docking and 3D drug design tasks, which exploits their duality by utilizing docking functionalities within the drug design process. Specifically, we represent 3D pocket-ligand complexes using parallel sequences of discrete tokens and continuous numbers, and we design a corresponding dual-channel transformer model to handle this format, thereby overcoming the challenges of 3D information modeling. Additionally, we alleviate data limitations through large-scale pre-training on a mixed dataset, followed by supervised and reinforcement learning fine-tuning techniques respectively tailored for the two tasks. Experimental results demonstrate that 3DMolFormer outperforms previous approaches in both protein-ligand docking and pocket-aware 3D drug design, highlighting its promising application in structure-based drug discovery. The code is available at: https://github.com/HXYfighter/3DMolFormer .

Related papers

• Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models [8.949206640442354]
  We introduce a large-scale structured reasoning dataset constructed via a novel slice-wise data synthesis paradigm.<n>Inspired by the genuine diagnostic workflow of radiologists, this paradigm models visual cognition by decomposing the complex 3D reading process.<n>This strategy unlocks deep volumetric understanding and highly interpretable clinical logic without requiring computationally expensive 3D-specific pre-training.
  arXiv  Detail & Related papers  (2026-07-15T14:05:58Z)
• MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models [59.180043227905294]
  We propose MedPruner, a training-free and model-agnostic hierarchical token pruning framework for efficient 3D medical image understanding.<n>We show that MedPruner enables models such as MedGemma to maintain or even exceed their original performance while retaining fewer than 5% of visual tokens.
  arXiv  Detail & Related papers  (2026-03-12T07:37:00Z)
• 3DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysis [42.29123264398027]
  3DMedAgent is a unified agent that enables 2D MLLMs to perform general 3D CT analysis without 3D-specific fine-tuning.<n> Experiments across over 40 tasks demonstrate that 3DMedAgent consistently outperforms general, medical, and 3D-specific MLLMs.
  arXiv  Detail & Related papers  (2026-02-20T08:31:26Z)
• Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models [0.0]
  We introduce a fully training-free framework for ZSAD in 3D brain MRI.<n>The framework constructs localized volumetric tokens by aggregating multi-axis slices processed by 2D foundation models.<n>These 3D patch tokens restore cubic spatial context and integrate directly with distance-based, batch-level anomaly detection pipelines.
  arXiv  Detail & Related papers  (2026-02-17T02:46:45Z)
• Lemon: A Unified and Scalable 3D Multimodal Model for Universal Spatial Understanding [80.66591664266744]
  Lemon is a unified transformer architecture that processes 3D point cloud patches and language tokens as a single sequence.<n>To handle the complexity of 3D data, we develop a structured patchification and tokenization scheme that preserves spatial context.<n>Lemon establishes new state-of-the-art performance across comprehensive 3D understanding and reasoning tasks.
  arXiv  Detail & Related papers  (2025-12-14T20:02:43Z)
• Abstract 3D Perception for Spatial Intelligence in Vision-Language Models [100.13033631690114]
  Vision-language models (VLMs) struggle with 3D-related tasks such as spatial cognition and physical understanding.<n>We introduce SandboxVLM, a framework that leverages abstract bounding boxes to encode geometric structure and physical kinematics for VLM.<n>Our approach consistently improves spatial intelligence, achieving an 8.3% gain on SAT Real compared with baseline methods.
  arXiv  Detail & Related papers  (2025-11-14T04:16:09Z)
• Semantic Causality-Aware Vision-Based 3D Occupancy Prediction [63.752869043357585]
  Vision-based 3D semantic occupancy prediction is a critical task in 3D vision.<n>Existing methods, however, often rely on modular pipelines.<n>We propose a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline.
  arXiv  Detail & Related papers  (2025-09-10T08:29:22Z)
• TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP [52.79100775328595]
  3D visual grounding allows an embodied agent to understand visual information in real-world 3D environments based on human instructions.<n>Existing 3D visual grounding methods rely on separate encoders for different modalities.<n>We propose a unified 2D pre-trained multi-modal network to process all three modalities.
  arXiv  Detail & Related papers  (2025-07-20T10:28:06Z)
• TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design [17.78777622199143]
  We propose TransDiffSBDD, an integrated framework combining autoregressive transformers and diffusion models for structure-based drug design (SBDD) Specifically, the autoregressive transformer models discrete molecular information, while the diffusion model samples continuous distributions, effectively resolving the first challenge. To address the second challenge, we design a hybrid-modal sequence for protein-ligand complexes that explicitly respects the causal relationship between modalities.
  arXiv  Detail & Related papers  (2025-03-26T18:35:34Z)
• GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency [50.11520458252128]
  Existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data.<n>We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models.<n>GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data.
  arXiv  Detail & Related papers  (2024-12-12T17:59:03Z)
• Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation [3.69758875412828]
  Cross-D Conv operation bridges the dimensional gap by learning the phase shifting in the Fourier domain.<n>Our method enables seamless weight transfer between 2D and 3D convolution operations, effectively facilitating cross-dimensional learning.
  arXiv  Detail & Related papers  (2024-11-02T13:03:44Z)
• Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design [21.666641467687214]
  We formulate dual-target drug design as a generative task and curate a novel dataset of potential target pairs based on synergistic drug combinations. We propose to design dual-target drugs with diffusion models that are trained on single-target protein-ligand complex pairs. Our algorithm can well transfer the knowledge gained in single-target pretraining to dual-target scenarios in a zero-shot manner.
  arXiv  Detail & Related papers  (2024-10-28T02:48:31Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Multi-task Learning with 3D-Aware Regularization [55.97507478913053]
  We propose a structured 3D-aware regularizer which interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space. We show that the proposed method is architecture agnostic and can be plugged into various prior multi-task backbones to improve their performance.
  arXiv  Detail & Related papers  (2023-10-02T08:49:56Z)
• Weakly-supervised 3D Pose Transfer with Keypoints [57.66991032263699]
  Main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies. We propose a novel weakly-supervised keypoint-based framework to overcome these difficulties.
  arXiv  Detail & Related papers  (2023-07-25T12:40:24Z)
• Interpretable 2D Vision Models for 3D Medical Images [47.75089895500738]
  This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods.
  arXiv  Detail & Related papers  (2023-07-13T08:27:09Z)
• Deep Generative Models on 3D Representations: A Survey [81.73385191402419]
  Generative models aim to learn the distribution of observed data by generating new instances. Recently, researchers started to shift focus from 2D to 3D space. representing 3D data poses significantly greater challenges.
  arXiv  Detail & Related papers  (2022-10-27T17:59:50Z)
• A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion [13.88656793940129]
  We propose an adaptive, identity-aware triangulation module to reconstruct 3D joints and identify the missing joints for each identity. We then propose a Dual-Masked Auto-Encoder (D-MAE) which encodes the joint status with both skeletal-structural and temporal position encoding for trajectory completion. In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset.
  arXiv  Detail & Related papers  (2022-07-15T10:00:43Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)

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.