{
  "id": 7375482,
  "title": "ForceFlowAb: physics-aware mixture-of-experts flow matching model for antibody CDRs design",
  "url": "https://urgent.news/2026/09/14/forceflowab-physics-aware-mixture-of-experts-flow-matching-model-for",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-14T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749983v1?rss=1"
  },
  "original_language": "en",
  "account": "Antibodies are crucial therapeutic molecules, with their target antigen recognition primarily driven by complementarity-determining regions (CDRs). CDRs are the focus of attention in antibody engineering, as they determine the binding specificity and affinity. Recent generative methods have enabled the co-design of CDR sequences and structures, tailored to a specific antigen. However, these methods struggle to capture local interface heterogeneity and do not have explicit energy-based guidance during sampling, leading to suboptimal antibody-antigen interaction energies.\n\nTo address these limitations, researchers have developed ForceFlowAb, a physics-aware mixture-of-experts flow-matching model for antigen-conditioned CDR sequence-structure co-design. ForceFlowAb incorporates an adaptive routing mechanism that models heterogeneous interface environments, enabling the system to handle diverse binding modes. The model also applies differentiable force-field guidance during sampling, steering the CDR generation towards energetically favorable conformations.\n\nWhen compared to existing methods such as FlowDesign and Diffab, ForceFlowAb demonstrates superior performance. For CDR-H3 design, ForceFlowAb achieved more favorable antibody-antigen interaction energies compared to both FlowDesign and Diffab, with improvement rates of 46.5% and 35.0% and 35.5%, respectively. In simultaneous six-CDR design, ForceFlowAb outperformed Diffab, with IMP values of 16% against 9%. These results indicate that ForceFlowAb combines the benefits of interface-adaptive modeling and energy-based guidance, capturing binding-mode diversity while ensuring biophysical feasibility through physical constraints.\n\nThe ForceFlowAb web server is freely available at http://zhanglab-bioinf.com/ForceFlowAb, and the source code and implementation are accessible at https://github.com/iobio-zjut/ForceFlowAb. For further inquiries, contact can be made at zgj@zjut.edu.cn. Supplementary data are also available at Bioinformatics online.",
  "summary": "Abstract Motivation: Antibodies are a major class of therapeutic molecules, and their recognition of target antigens is largely mediated by complementarity-determining regions (CDRs), making antigen-conditioned CDR design a central problem in antibody engineering. Recent generative methods have enabled antigen-conditioned co-design of CDR sequences and structures, but their limited capacity to…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}