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AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion

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arxiv 2505.12358 v1 pith:X4ICHQOD submitted 2025-05-18 cs.LG cs.AI

AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion

Abrar Rahman Abir , Haz Sameen Shahgir , Md Rownok Zahan Ratul , Md Toki Tahmid , Greg Ver Steeg , Yue Dong This is my paper
classification cs.LG cs.AI
keywords energybindingabflownetdiffusionantibodycomputationallyexpensiveframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Complementarity Determining Regions (CDRs) are critical segments of an antibody that facilitate binding to specific antigens. Current computational methods for CDR design utilize reconstruction losses and do not jointly optimize binding energy, a crucial metric for antibody efficacy. Rather, binding energy optimization is done through computationally expensive Online Reinforcement Learning (RL) pipelines rely heavily on unreliable binding energy estimators. In this paper, we propose AbFlowNet, a novel generative framework that integrates GFlowNet with Diffusion models. By framing each diffusion step as a state in the GFlowNet framework, AbFlowNet jointly optimizes standard diffusion losses and binding energy by directly incorporating energy signals into the training process, thereby unifying diffusion and reward optimization in a single procedure. Experimental results show that AbFlowNet outperforms the base diffusion model by 3.06% in amino acid recovery, 20.40% in geometric reconstruction (RMSD), and 3.60% in binding energy improvement ratio. ABFlowNet also decreases Top-1 total energy and binding energy errors by 24.8% and 38.1% without pseudo-labeling the test dataset or using computationally expensive online RL regimes.

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Cited by 4 Pith papers

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  1. ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning

    cs.LG 2026-05 unverdicted novelty 7.0

    ConTact decomposes CDR design into surface fingerprint learning, contact prediction, and contact-gated sequence generation using distance-biased attention and weighted loss, reporting 7% RMSD and 10% F1 gains on CHIME...

  2. ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning

    cs.LG 2026-05 unverdicted novelty 7.0

    ConTact introduces a contact-then-act architecture with distance-biased cross-attention and contact-weighted loss for antibody CDR design, reporting 5-6% better backbone RMSD and superior contact metrics on CHIMERA-Be...

  3. AgForce Enables Antigen-conditioned Generative Antibody Design

    cs.LG 2026-05 unverdicted novelty 6.0

    AgForce improves antigen-conditioned antibody design by using framework dropout, gated bottlenecks, hyperbolic cross attention, MDN sequence head with Potts-like coupling, annealed MCL, and antigen cycle consistency t...

  4. EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation

    cs.LG 2026-05 unverdicted novelty 6.0

    EvoStruct integrates evolutionary priors from a protein language model with structural priors from an E(3)-equivariant GNN to raise amino acid recovery by 16% and diversity by 2.3x on CHIMERA-Bench while cutting perpl...