Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process. Structure-based drug similarities and sequence-based target protein similarities are the commonly used information for DTI prediction. Among numerous computational methods, neighborhood-based chemogenomic approaches that leverage drug and target similarities to perform predictions directly are simple but promising ones. However, existing similarity-based methods need to be re-trained to predict interactions for any new drugs or targets and cannot directly perform predictions for both new drugs, new targets, and new drug-target pairs. Furthermore, a large amount of missing (undetected) interactions in current DTI datasets hinders most DTI prediction methods. To address these issues, we propose a new method denoted as Weighted k-Nearest Neighbor with Interaction Recovery (WkNNIR). Not only can WkNNIR estimate interactions of any new drugs and/or new targets without any need of re-training, but it can also recover missing interactions (false negatives). In addition, WkNNIR exploits local imbalance to promote the influence of more reliable similarities on the interaction recovery and prediction processes. We also propose a series of ensemble methods that employ diverse sampling strategies and could be coupled with WkNNIR as well as any other DTI prediction method to improve performance. Experimental results over five benchmark datasets demonstrate the effectiveness of our approaches in predicting drug-target interactions. Lastly, we confirm the practical prediction ability of proposed methods to discover reliable interactions that were not reported in the original benchmark datasets.
RCC Institute of Information Technology（RCC信息技术学院）；Aliah University（阿里亚大学）
In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we …
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Gaoling School of AI (GSAI), Renmin University of China（中国人民大学高瓴人工智能学院（GSAI））；Microsoft Research（微软研究院）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；University of Science and Technology of China（中国科学技术大学）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education（教育部下一代智能搜索与推荐工程研究中心）
DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
Regenerative Medicine Research Center, Isfahan University of Medical Sciences（再生医学研究中心，伊斯法罕医科大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences（伊斯法罕神经科学研究中心，伊斯法罕医科大学）；Department of Electrical and Computer Engineering, Isfahan University of Technology（电气与计算机工程系，伊斯法罕技术大学）
Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE model itself suffers from deficiencies such as poor manifold recovery when data lie on low-dimensional manifold embedded in higher dimensional ambient space and they manifest themselves in each applications differently. The consequences of it in drug discovery is somewhat under-explored. In this paper, we study how to improve the similarity of the data generated via VAE and the training dataset by improving manifold…
National Sun Yat-sen University（国立中山大学）；Kaohsiung Medical University（高雄医学大学）
Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events. Therefore, early identification of AKI may improve renal function recovery, decrease comorbidities, and further improve patients' survival. To control certain risk factors and develop targeted prevention strategies are important to reduce the risk of AKI. Drug-drug interactions and drug-disease interactions are critical issues for AKI. Typical statistical approaches cannot handle the complexity of drug-drug and drug-disease interactions. In this paper, we propose a novel learning algorithm, Dee…
University of Pennsylvania（宾夕法尼亚大学）；Yale University（耶鲁大学）；Duke University（杜克大学）
Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$-nearest neighbor (NN) graph in an $n$-vertex complete graph, whose edge weights are independent and distributed according to $P_n$ for edges in the hidden $2k$-NN graph and $Q_n$ otherwise. The special case of Bernoulli distributions corresponds to a variant of the Watts-Strogatz small-world graph. We focus on two types of asymptotic recovery guarantees as $n\to \infty$: (1) exact recovery: all edges are classified correctl…
Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search
UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Eshelman School of Pharmacy, UNC Chapel Hill（北卡罗来纳大学教堂山分校埃谢尔曼药学院）；Department of Pharmacology, UNC Chapel Hill（北卡罗来纳大学教堂山分校药理学系）；Joint Department of Biomedical Engineering at UNC Chapel Hill and NCSU（北卡罗来纳大学教堂山分校与北卡罗来纳州立大学生物医学工程联合系）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Nearest neighbor (k-NN) graphs are widely used in machine learning and data mining applications, and our aim is to better understand what they reveal about the cluster structure of the unknown underlying distribution of points. Moreover, is it possible to identify spurious structures that might arise due to sampling variability? Our first contribution is a statistical analysis that reveals how certain subgraphs of a k-NN graph form a consistent estimator of the cluster tree of the underlying distribution of points. Our second and perhaps most important contribution is the following finite samp…
Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）；Tübingen AI Center, University of Tübingen（蒂宾根大学蒂宾根人工智能中心）；Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy（柏林夏里特医科大学精神病学与心理治疗系）；German Center for Mental Health (DZPG), partner site Tübingen（德国精神卫生中心（DZPG）蒂宾根合作站点）
The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, which is why in practice one often applies a kNN classifier on top of a (pre-trained) feature transformation. From a theoretical perspective, most, if not all theoretical results aimed at understanding the kNN classifier are derived for the raw feature space. This leads to an emerging gap between our theoretical understanding of kNN and its practical applications. In this paper, we take a first step towards bridging thi…
Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
Dept. of Comp. Sci. & Tech., Tsinghua University（计算机科学与技术系，清华大学）；Institute for AIR, Tsinghua University（空气智能研究所，清华大学）；Gaoling School of Artificial Intelligence, Renmin University of China（北京理工大学人工智能学院）
