Liquid-liquid phase separation (LLPS) has emerged as a fundamental mechanism underlying the formation and regulation of membraneless cellular compartments and is increasingly implicated in diverse physiological processes and diseases. Alongside rapid experimental and high-throughput advances, bioinformatics data resources and computational models have expanded substantially, enabling systematic cataloguing of LLPS-associated components and prediction of phase-separation behavior from molecular features. However, the resulting computational landscape remains highly fragmented. In this review, we provide a comprehensive and critical synthesis of bioinformatics resources and predictive modelling approaches for LLPS. We examine and compare major LLPS databases, highlighting differences in evidence types, curation strategies, coverage, and cross-resource inconsistencies that limit integrative analysis. We then survey computational models across core LLPS prediction tasks, encompassing more than 40 representative algorithms and tracing methodological evolution from classical machine learning to deep learning and large language model-based frameworks. By integrating these advances, we identify a fundamental mismatch between molecule-centric data abstractions and the inherently multicomponent, context-dependent organization of LLPS phenomena. We argue that future progress may benefit from event-centric frameworks that explicitly represent molecular assemblies, contextual conditions and observable phase behaviors, thereby providing a coherent foundation for next-generation LLPS datasets and computational models with improved mechanistic interpretability and translational relevance.

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Many studies have proved that small nucleolar RNAs (snoRNAs) play critical roles in the development of various human complex diseases. Discovering the associations between snoRNAs and diseases is an important step toward understanding the pathogenesis and characteristics of diseases. However, uncovering associations via traditional experimental approaches is costly and time-consuming. This study proposed a bounded nuclear norm regularization-based method, called PSnoD, to predict snoRNA-disease associations. Benchmark experiments showed that compared with the state-of-the-art methods, PSnoD achieved a superior performance in the 5-fold stratified shuffle split. PSnoD produced a robust performance with an area under receiver-operating characteristic of 0.90 and an area under precision-recall of 0.55, highlighting the effectiveness of our proposed method. In addition, the computational efficiency of PSnoD was also demonstrated by comparison with other matrix completion techniques. More importantly, the case study further elucidated the ability of PSnoD to screen potential snoRNA-disease associations. The code of PSnoD has been uploaded to https://github.com/linDing-groups/PSnoD. Based on PSnoD, we established a web server that is freely accessed via http://psnod.lin-group.cn/.

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).

MOTIVATION: Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value. RESULTS: In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. AVAILABILITY: Cellinker is available at http://www.rna-society.org/cellinker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available. RESULTS: RNA Interactome Scoper (RIscoper) represents the first tool for full-scale RNA interactome scanning and was developed for extracting RRIs from the literature based on the N-gram model. Notably, a reliable RRI corpus was integrated in RIscoper, and more than 13 300 manually curated sentences with RRI information were recruited. RIscoper allows users to upload full texts or abstracts, and provides an online search tool that is connected with PubMed (PMID and keyword input), and these capabilities are useful for biologists. RIscoper has a strong performance (90.4% precision and 93.9% recall), integrates natural language processing techniques and has a reliable RRI corpus. AVAILABILITY AND IMPLEMENTATION: The standalone software and web server of RIscoper are freely available at www.rna-society.org/riscoper/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.