AI Model for Age-Related Disease Target Discovery | Aging-US

Posted by

Aging (Aging-US)

Aging-US published this trending research paper on September 22, 2023, in Volume 15, Issue 18, entitled, “Biomedical generative pre-trained based transformer language model for age-related disease target discovery" by researchers from Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China; Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, UAE. DOI - https://doi.org/10.18632/aging.205055 Corresponding author - Alex Zhavoronkov - alex@insilico.com Abstract Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field. Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.205055 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, transformers, deep learning, therapeutic target discovery, aging biomarkers, human aging About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM