Recent years have witnessed the surge of academic researches and industrial implementations of succinct non-interactive arguments of knowledge (SNARKs). However, proving time remains a bottleneck for applying SNARKs to large-scale circuits. To accelerate the proof generation process, a promising way is to distribute the workload to several machines running in parallel, the SNARKs with which feature are called distributed SNARKs. Nevertheless, most existing works either require a trusted setup, or rely on quantum-insecure assumptions, or suffer from linear communication costs.

Toxicity risk assessment plays a crucial role in determining the clinical success and market potential of drug candidates. Traditional animal-based testing is costly, time-consuming, and ethically controversial, which has led to the rapid development of computational toxicology. This review surveys over 20 ADMET prediction platforms, categorizing them into rule/statistical-based methods, machine learning (ML) methods, and graph-based methods. We also summarize major toxicological databases into four types: chemical toxicity, environmental toxicology, alternative toxicology, and biological toxin databases, highlighting their roles in model training and validation. Furthermore, we review recent advancements in ML and artificial intelligence (AI) applied to toxicity prediction, covering acute toxicity, organ-specific toxicities, and carcinogenicity. The field is transitioning from single-endpoint predictions to multi-endpoint joint modeling, incorporating multimodal features. We also explore the application of generative modeling techniques and interpretability frameworks to improve the accuracy and credibility of predictions. Additionally, we discuss the use of network toxicology in evaluating the safety of traditional Chinese medicines (TCMs) and the potential of large language models (LLMs) in literature mining, knowledge integration, and molecular toxicity prediction. Finally, we address current challenges, including data quality, model interpretability, and causal inference, and propose future directions such as multi-omics integration, interpretable AI models, and domain-specific LLMs, aiming to provide more efficient and precise technical support for preclinical toxicity assessments in drug development.

The design of zkSNARKs is increasingly complicated and requires familiarity with a broad class of cryptographic and algebraic tools. This complexity in zkSNARK design also increases the difficulty in zkSNARK implementation, analysis, and optimization. To address this complexity, we develop a new workflow for designing and implementing zkSNARKs, called VOProof. In VOProof, the designer only needs to construct a Vector Oracle (VO) protocol that is intuitive and straightforward to design, and then feeds this protocol to our VO compiler to transform it into a fully functional zkSNARK. This new workflow conceals most algebraic and cryptographic operations inside the compiler, so that the designer is no longer required to understand these cumbersome and error prone procedures. Moreover, our compiler can be fine-tuned to compile one VO protocol into multiple zkSNARKs with different tradeoffs.

Ring confidential transaction (RingCT) protocol is widely used in cryptocurrency to protect the privacy of both users’ identities and transaction amounts. Most recently, a new RingCT protocol (called RingCT 2.0) was proposed by leveraging cryptographic accumulators, which can achieve a constant-size output theoretically but still far from being practical due to the heavy zero-knowledge associated with the accumulator. In this article, we revisit the design of ring confidential transaction protocol and put forward a more efficient privacy-preserving payment protocol, which is built upon an extended version of one-out-of-many proof and a special multi-signature. Compared with previous works, the new protocol is not only more practical, but also does not suffer from a trusted setup. Besides, we show that the protocol satisfies the security requirements provided that the underlying cryptographic primitives are secure in the random oracle model. We implement our new payment protocol in Java, and the experimental results show that it is efficient enough to be used in practice.

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.