In the field of life and health sciences, the DeepWism® platform has recently demonstrated exceptional capabilities in scientific hypothesis generation. Aging is a multifaceted biological process governed by complex molecular pathways, posing significant challenges for the development of effective therapeutic interventions.
In a joint study conducted by DeepWism Intelligence, Allife Medicine Co., Ltd, the University of Cambridge, and the Wellcome Sanger Institute, we introduced an AI-powered platform that integrates semantic indexing, biomedical knowledge graphs, and the proprietary DeepRanking algorithm to identify miRNAs with potential anti-aging effects—marking a major breakthrough in gene-based therapeutic discovery.
A joint manuscript is currently being prepared by the collaborating institutions for submission to a peer-reviewed journal, aiming to contribute these findings to the ongoing scientific discourse on aging and RNA therapeutics.
The platform generated 100 scientific hypotheses, which were subjected to multiple rounds of wet-lab validation, achieving a 71% success rate—a substantial improvement over the ~10% success rate typically seen with human expert approaches. Notably, one of the validated hypotheses involved exosome-mediated delivery of a specific miRNA, which was shown to significantly suppress cellular senescence and promote collagen remodeling. This candidate has now advanced to clinical trials, establishing a new paradigm for AI-driven miRNA therapeutics.
At its core, DeepWism® R2 is built upon the Thin-Thick-Thin Crowd Entropy Dynamics System (T3CEDS) framework, which introduces entropy reduction as the key mechanism for intelligent reasoning. This enables the transformation of miRNA research from trial-and-error under high entropy, into a closed-loop of precise hypothesis generation and validation.
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Thin Perception Layer
Efficiently captures and encodes multi-modal inputs (e.g., publications, patents, omics data), significantly reducing input entropy. -
Thick Processing Layer
Uses Crowd intelligence to perform structured reasoning and deep analysis, systematically lowering entropy while uncovering high-potential therapeutic candidates. -
Thin Decision Layer
Condenses complex reasoning outputs into high-confidence hypotheses and actionable experimental plans.
Unlike traditional attention-based AI models, DeepWism® R2 is built around entropy management. This design enhances data integration, hypothesis generation, and interpretability—addressing complexity and uncertainty in biomedical research.
The Thick Processing Layer applies collaborative reasoning across multi-source data, enabling the accurate identification and prioritization of novel miRNA targets under high-entropy constraints.
DeepWism® R2 is applicable across various domains such as anti-aging medicine, oncology, metabolic disorders, and neurological diseases—empowering biotech teams to rapidly expand their discovery pipelines.
DeepWism® R2 has been successfully deployed in collaborative research projects with leading medical institutions, validating its effectiveness in miRNA drug discovery.
Figure 1: Knowledge-Integrated miRNA Candidate Identification Architecture
DeepWism® R2 integrates literature sources and biomedical databases to build a semantic index and knowledge graph. Through literature retrieval, entity ranking (DeepRanking), and filtering modules, it intelligently identifies and prioritizes miRNA candidates associated with aging.
DeepWism® R2 accurately identified key miRNA targets, which were experimentally validated via exosome delivery in both cellular and animal models—demonstrating effects such as wrinkle reduction, collagen remodeling, and p16/p53 downregulation.
The candidates advanced quickly to clinical trials, where early results showed significant skin rejuvenation and anti-aging effects—setting a new standard for AI + miRNA-based therapeutic innovation.
DeepWism® is now offering free advanced research services to all global research institutions and innovative drug development companies.
To request access, please email r2@deepwism.com or visit i.deepwism.com to obtain a DeepWism invitation code.
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