Accurate antibody affinity prediction is crucial for drug discovery. However, it remains challenging due to data scarcity, which limits the performance of deep learning models in few-shot, antigen-specific scenarios. To address this challenge, we propose MetaPrior, a novel meta-learning framework designed to learn robust prior knowledge before fine-tuning. The core of MetaPrior is a teacher-student mechanism where a Meta Network (teacher) is trained via bilevel optimization to generate high-quality labels for a diverse set of pseudo-samples and a binding affinity predictor (student) learns from these labels. The teacher is optimized by the student's performance on a small and experimentally measured guide set, enabling the pseudo-samples to approximate the true data distribution under conditions of data scarcity. Extensive experiments demonstrate that MetaPrior is compatible with popular network architectures (e.g., MLPs, CNN s and Transformers) and significantly surpasses standard fine-tuning strategies, particularly in data-scarce scenarios. Across several antigen-specific datasets, such as VEGF, MetaPrior achieves a notable performance gain even with only 40% of the available training data, increasing the Pearson correlation coefficient from 0.38 to 0.58 (a 53% improvement) and enhancing training stability by reducing the standard deviation from 0.42 to 0.19 (a 54.8% reduction in variability). These results confirm the robustness and generalizability of our proposed method. By jointly optimizing pseudo-labels and affinity learning, MetaPrior aligns synthetic supervision with the true data distribution, offering a principled solution for few-shot antibody affinity prediction.

The design of nanobodies with high binding affinity for a given target is the primary objective in the therapeutics development. However, existing generative models often sample the sequence space of nanobody without explicit guidance, which leads to suboptimal affinity. To address this, we introduce DiffNB, a novel diffusion-based framework for controllable, high-affinity nanobody generation. DiffNB is the first diffusion model that leverages Direct Preference Optimization (DPO) to align a pre-trained generative prior with desired bio-physical properties. By fine-tuning the model on preference pairs of high- and low-affinity nanobodies, DiffNB learns to characterize and generate the variants with higher binding affinity and other bio-physical properties improved. During generation, the DPO-aligned DiffNB co-designs the sequence and structure of CDR regions to produce optimized and novel candidates. In our extensive experiments on three therapeutic antigens(HER2, IL-6, and CD45), we demonstrate that DiffNB can generate nanobodies exhibiting higher binding affinity and better structural diversity. Compared to the state-of-the-art baselines, our DPO-guided generation improves the “in silico” binding affinity by up to 35%, structural diversity by up to 46%, stability by up to 8%, and humanness by up to 25%. Our work establishes DPO as a powerful and efficient paradigm for steering generative models in nanobody design, paving the way for targeted and accelerated drug discovery.

Nanobodies are promising therapeutic agents due to their superior biological properties. Given the importance of binding affinity, a computational model capable of generating high-affinity nanobodies can significantly accelerate the design process. However, two key challenges remain: 1) integrating fragmented sequence data to pre-train robust nanobody representations, and 2) augmenting the limited nanobody-antigen interaction datasets. In this paper, we introduce NanoGen, a high-affinity nanobody generation model utilizing guided diffusion within a two-stage training framework. In the pre-training phase, we curate large-scale datasets that include both heavy-chain antibody and nanobody data for representation learning. In the fine-tuning stage, we implement a pipeline that augments nanobody-antigen binding data to further refine the pre-trained model. Through nanobody sequence pre-training and affinity-specific fine-tuning, NanoGen outperforms established baselines in both sequence infilling and affinity optimization tasks, demonstrating its potential to advance nanobody design and therapeutic development.