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Abstract Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA’s domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation.

Abstract Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.

Colorectal cancer (CRC), a leading cause of cancer‐related deaths globally, demands innovative therapeutic strategies to improve patient outcomes. Drug repurposing, identifying new uses for existing drugs, provides a cost‐effective solution. To this end, this study constructs the first drug‐target affinity dataset specifically for two novel therapeutic targets for CRC, P2X4 and mTOR, and designs a new deep learning‐based multilevel feature aggregation enhanced (MFAE) model. The model implements hierarchical feature extraction and multilevel feature aggregation and enhancement to simulate complex drug‐target interactions. Evaluations using fivefold cross‐validation on the collected CRC dataset showcase MFAE's superior predictive accuracy. Fine‐tuning of the model on external experimental data further enhances its performance, with a concordance index of 0.930, a determination coefficient of 0.782, and a mean squared error of 0.191. Ablation studies further highlight the key role of the group‐wise feature enhancement mechanism and ensemble learning strategy in enhancing the model's performance. Virtual screening of the Food and Drug Administration‐approved drugs identifies Ponatinib and Talazoparib as potential repurposing candidates. Despite limitations in experimental validation, this study establishes an innovative computational framework designed for CRC drug discovery. Overall, this research offers a valuable perspective on leveraging computational approaches for precision oncology.

The article introduces DeepAIR, a deep learning framework that combines sequence and 3D structure features of adaptive immune receptors (AIRs) for accurate prediction of their binding affinity to antigens. DeepAIR achieves high predictive performance, with correlation coefficients of 0.813 for T cell receptors (TCRs) and median AUCs of 0.904 and 0.942 for TCR and B cell receptor (BCR) binding reactivity, respectively.