Drug combinations have exhibited promise in treating cancers with less toxicity and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is time-consuming and labor-intensive because of the combinatorial explosion. Although a number of computational methods have been developed for predicting synergistic drug combinations, the multi-way relations between drug combinations and cell lines existing in drug synergy data have not been well exploited. We propose a multi-way relation-enhanced hypergraph representation learning method to predict anti-cancer drug synergy, named HypergraphSynergy. HypergraphSynergy formulates synergistic drug combinations over cancer cell lines as a hypergraph, in which drugs and cell lines are represented by nodes and synergistic drug–drug–cell line triplets are represented by hyperedges, and leverages the biochemical features of drugs and cell lines as node attributes. Then, a hypergraph neural network is designed to learn the embeddings of drugs and cell lines from the hypergraph and predict drug synergy. Moreover, the auxiliary task of reconstructing the similarity networks of drugs and cell lines is considered to enhance the generalization ability of the model. In the computational experiments, HypergraphSynergy outperforms other state-of-the-art synergy prediction methods on two benchmark datasets for both classification and regression tasks and is applicable to unseen drug combinations or cell lines. The studies revealed that the hypergraph formulation allows us to capture and explain complex multi-way relations of drug combinations and cell lines, and also provides a flexible framework to make the best use of diverse information. The source data and codes of HypergraphSynergy can be freely downloaded from https://github.com/liuxuan666/HypergraphSynergy. Supplementary data are available at Bioinformatics online.

Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive node roles, hindering the acquisition of effective link representations. Subgraph-based methods have been introduced as solutions but often ignore shared information among subgraphs. To address these limitations, we propose a Subgraph-aware Graph Kernel Neural Network (SubKNet) for link prediction in biological networks. Specifically, SubKNet extracts a subgraph for each node pair and feeds it into a graph kernel neural network, which decomposes each subgraph into a combination of trainable graph filters with diversity regularization for subgraph-aware representation learning. Additionally, node embeddings of the network are extracted as auxiliary information, aiding in distinguishing node pairs that share the same subgraph. Extensive experiments on five biological networks demonstrate that SubKNet outperforms baselines, including methods especially designed for biological networks and methods adapted to various networks. Further investigations confirm that employing graph filters to subgraphs helps to distinguish node roles in different subgraphs, and the inclusion of diversity regularization further enhances its capacity from diverse perspectives, generating effective link representations that contribute to more accurate link prediction.

Drug response prediction (DRP) is a longstanding challenge in modern oncology that underpins personalized treatment. Early DRP methods, trained on label-rich cell line samples, suffer from performance degradation when applied to label-scarce patient samples due to the distribution shift. Recently, a few transfer learning efforts have addressed this issue by aligning cell line (source domain) and patient (target domain) data via unsupervised domain adaptation (UDA). However, these efforts often treat each drug's response prediction as an isolated task, requiring model retraining when the drug changes; and focus only on aligning data distributions as a whole, neglecting the category (e.g., different cancers or tissues) confusion problem. To address these limitations, we propose a knowledge-guided domain adaptation model to transfer the DRP from cell lines to patients, named TransDRP. Specifically, TransDRP operates in two phases: pre-training and adaptation. In the first phase, we pre-train a multi-label graph neural network using molecular knowledge, to simultaneously predict responses for various drugs and capture their interdependencies. In the second phase, we implement a global-local domain adversarial strategy with clinical knowledge, to encourage representation alignment within same cancer categories and separation among different cancer categories across domains. Extensive experiments demonstrate that TransDRP outperforms state-of-the-art UDA methods in both transfer efficiency and precision for the patient DRP.

Antibody lead discovery, crucial for immunotherapy development, requires identifying candidates with potent binding affinities to target antigens. Recent advances in protein language models have opened promising avenues to tackle this challenge by predicting antibody–antigen interactions (AAIs). Despite their appeals, precisely detecting binding sites (i.e., paratopes and epitopes) within the complex landscape of long-sequence biomolecules remains challenging. Herein, we propose MambaAAI, a bio-inspired model built upon the Mamba architecture, designed to predict AAIs and identify binding sites through selective attention mechanisms. Technically, we employ ESM-2, a pre-trained protein language model to extract evolutionarily enriched representations from input antigen and antibody sequences, which are modeled as residue-level interaction matrixes. Subsequently, a dual-view Mamba encoder is devised to capture important binding patterns, by dynamically learning embeddings of interaction matrixes from both antibody and antigen perspectives. Finally, the learned embeddings are decoded using a multilayer perceptron to output interaction probabilities. MambaAAI provides a unique advantage, relative to prior techniques, in dynamically selecting bio-enhancing residue sites that contribute to AAI prediction. We evaluate MambaAAI on two large-scale antibody–antigen neutralization datasets, and in silico results demonstrate that our method marginally outperforms the state-of-the-art baselines in terms of prediction accuracy, while maintaining robust generalization to unseen antibodies and antigens. In further analysis of the selective attention mechanism, we found that MambaAAI successfully uncovers critical epitope and paratope regions in the SARS-CoV-2 antibody examples. It is believed that MambaAAI holds great potential to discover lead candidates targeting specific antigens at a lower burden.

Using computational methods to personalize drug response prediction holds great promise to improve cancer therapy. Most existing methods use either biochemical information or response-related networks to predict drug response, nevertheless, the information they considered is not comprehensive. In this study, we present a novel end-to-end deep learning-based method Graph Neural Network with multi-task learning for Drug Response Prediction (GNNDRP). It leverages biochemical features as well as the hidden features from the heterogeneous network which incorporates the known drug-cell line responses, drug similarities, and cell line similarities, to complete the drug response prediction task. Moreover, GNNDRP designs a self-supervised task to enhance the representation capacity from the response network and further improve the model prediction performance. Extensive experiments show that GNNDRP outperforms existing state-of-the-art prediction methods under various experimental settings. The ablation analysis reveals that the biochemical characteristics, response-related network, and our self-supervised strategy can boost the predictive power. Additionally, case studies further validate the effectiveness of GNNDRP in identifying novel drug-cell line responses.

Anticancer peptides (ACPs) have emerged as a promising class of therapeutic molecules due to their low toxicity and high specificity. However, experimentally identifying ACPs is labor-intensive and low-throughput, making virtual screening with intelligent models an attractive alternative. Despite recent progress, the number of ACP samples available in existing databases remains limited, which may cause predictive models to overfit the labeled information and exhibit suboptimal generalization. To address this issue, we propose a semi-supervised prediction framework with bio-inspired data augmentation for ACP identification, called AugACP. Specifically, we incorporate semantic knowledge into masked Transformer pretraining to guide ACP sequence augmentation and provide more reliable supervised signals. Based on the augmented data, we adopt a semi-supervised learning strategy that selects high-confidence pseudo-labels from augmented peptides to improve prediction performance. In the computational experiments, AugACP outperforms state-of-the-art baseline methods under various experimental configurations, particularly in generalizing to unseen oligopeptides. Moreover, t-SNE visualizations indicate that AugACP learns more discriminative embeddings for separating ACPs from non-ACPs, while external validation on novel ACPs also achieves an F1-score of 0.9272. These results highlight the predictive power of AugACP and its potential value in promoting anticancer drug discovery.