Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations. In contrast, BioBridge designs an Inductive-Associative pipeline inspired by the workflow of scientists who base their accumulated expertise on drawing insights into novel drug-target pairs from weakly related references. BioBridge predicts novel drug-target interactions using limited sequence data, incorporating multi-level encoders with adversarial training to accumulate transferable binding principles. On these principles basis, BioBridge employs a dynamic prototype meta-learning framework to associate insights from weakly related annotations, enabling robust predictions for previously unseen drug-target pairs. Extensive experiments demonstrate that BioBridge surpasses existing models, especially for unseen proteins. Notably, when only homologous protein binding data is available, BioBridge proves effective for virtual screening of the epidermal growth factor receptor and adenosine receptor, underscoring its potential in drug discovery.
Sun Yat-sen University（中山大学）；Galixir Technologies Ltd（Galixir科技有限公司）
Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existing methods either heavily rely on high-quality domain features or are intrinsically transductive, which limits the capacity of models to generalize to drugs/genes that lack external information or are unseen during the training process. To address these problems, we propose a nove…
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
Nostrum Biodiscovery（诺斯特伦生物发现公司）；Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology（巴塞罗那科学技术研究所生物医药研究所（IRB巴塞罗那））；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning
School of Mathematical Sciences（数学科学学院）；Shanghai Key Laboratory of PMMP（PMMP上海重点实验室）；East China Normal University（华东师范大学）；Shanghai（上海）；China（中国）
Associative Learning Mechanism for Drug-Target Interaction Prediction
Chongqing University of Posts and Telecommunications（重庆邮电大学）；State University of New York at Buffalo State（纽约州立大学布法罗州立学院）；DH (Shanghai) Diagnostics Co.,Ltd.（DH（上海）诊断有限公司）
BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
Department of Statistics University of South Carolina（统计学系南卡罗来纳大学）；Department of Biostatistics University of South Carolina（生物统计学系南卡罗来纳大学）；Department of Biostatistics University of Florida（生物统计学系佛罗里达大学）；Department of Surgery University of Florida（外科医学系佛罗里达大学）；Department of Statistics George Mason University（统计学系乔治·梅森大学）
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to rare types such as the case in repurposing drugs for novel diseases. This motivates the task of few-shot link prediction. Typically, GCNs are ill-equipped in learning such rare link types since the relation embedding is not learned in an inductive fashion. This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime. The…
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network, IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug r…
SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction
School of Medicine, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）医学院）；Warshel Joint Laboratory for skin health and active molecule innovation（皮肤健康与活性分子创新瓦谢尔联合实验室）；Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）广东省数字生物学与药物开发重点实验室）
QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
Department of Computer Science（计算机科学系）；University of Birmingham（伯明翰大学）；QunaSys Europe（QunaSys欧洲分公司）；Hon Hai Research Institute（鸿海研究机构）；Departement of Computer Science（计算机科学系）
Towards a more inductive world for drug repurposing approaches
CIMA University of Navarra（纳瓦拉大学CIMA）；IdiSNA（纳瓦拉卫生研究所（IdiSNA））；TECNUN, University of Navarra（纳瓦拉大学TECNUN）；Stanford Center for Biomedical Informatics Research, Stanford University（斯坦福大学生物医学信息学研究中心）；Ghent University（根特大学）；Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra（纳瓦拉大学数据科学与人工智能研究所（DATAI））
Hunan University（湖南大学）；Microsoft Research Asia（微软亚洲研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment. Previous studies usually model drug information constrained on a single view such as the drug itself, leading to incomplete and noisy information, which limits the accuracy of DDI prediction. In this work, we propose a novel multi- view drug substructure network for DDI prediction (MSN-DDI), which learns chemical substructures from both the representations of the single drug (intra-view) and the drug pair (inter-view) simultaneously and utilizes the substructures to update the drug…
This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm. We use the SVM+ realization of LUPI in an inductive conformal predictor, and apply it to the MNIST benchmark dataset and three datasets in drug discovery. The results show that using privileged information produces valid models and improves efficiency compared to standard SVM, however the improvement varies between the tested datasets and is not substantial in the drug discovery applications. More importantly, using SVM+ in a conformal prediction framework enables valid prediction intervals a…
Drug repositioning is an attractive cost-efficient strategy for the development of treatments for human diseases. Here, we propose an interpretable model that learns disease self-representations for drug repositioning. Our self-representation model represents each disease as a linear combination of a few other diseases. We enforce proximity in the learnt representations in a way to preserve the geometric structure of the human phenome network - a domain-specific knowledge that naturally adds relational inductive bias to the disease self-representations. We prove that our method is globally opt…
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Life Sciences, Northwestern Polytechnical University（西北工业大学生命学院）
Motivation: Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore that the action of a drug is mainly caused by its chemical substructures. In addition, their interpretability is still weak. Results: In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (sub-structures) and their DDI types are determined by the linkages between different substructure sets, we design a nov…
DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction
Department of Computer Science（计算机科学系）；North Carolina State University（北卡罗来纳州立大学）；UNC School of Medicine（UNC医学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unsee…
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favourably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically-accessible drug-like molecules. This becomes possible by defining transitions in our Markov Decision Proce…
Learning Protein-Ligand Binding in Hyperbolic Space
Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（人工智能产业研究院（AIR），清华大学，北京，中国）；University of Electronic Science and Technology of China, Chengdu, China（电子科技大学，成都，中国）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（计算机科学与技术系，清华大学，北京，中国）；Beijing Academy of Artificial Intelligence, Beijing, China（北京人工智能研究院，北京，中国）
