A Framework for Autonomous AI-Driven Drug Discovery DOI Creative Commons

Douglas W. Selinger,

Tom Wall,

Eleni Stylianou

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract The exponential increase in biomedical data offers unprecedented opportunities for drug discovery, yet overwhelms traditional analysis methods, limiting the pace of new development. Here we introduce a framework autonomous artificial intelligence (AI)-driven discovery that integrates knowledge graphs with large language models (LLMs). It is capable planning and carrying out automated programs while providing details its research strategy, progress, supporting points, enabling thorough assessment methods findings. At heart this lies “focal graph” - novel construct harnesses centrality algorithms to distill vast, noisy datasets into concise, transparent, data-driven hypotheses. By high-throughput search result interpretation, such could be used execute massive numbers searches, identify patterns across complex, diverse datasets, prioritize actionable hypotheses at scale speed unachievable by human researchers alone. We demonstrate even small- applications approach can yield novel, transparent insights relevant multiple stages process present prototype system autonomously executing multi-step target workflow. focal graph described here, automation it enables, represents promising path forward: towards deeper understanding mechanisms underlying disease true acceleration development therapeutics. Graphical

Language: Английский

Agentic Large Language Models for Healthcare: Current Progress and Future Opportunities DOI Creative Commons
Han Yuan

Medicine Advances, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

Language: Английский

Citations

3
Bioinformatics and biomedical informatics with ChatGPT: Year one review DOI Open Access
Jinge Wang,

Zien Cheng,

Qiuming Yao

et al.

Quantitative Biology, Journal Year: 2024, Volume and Issue: 12(4), P. 345 - 359

Published: June 27, 2024

Abstract The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre‐trained Transformer (ChatGPT), across various disciplines. We surveyed application ChatGPT bioinformatics and biomedical informatics throughout year, covering omics, genetics, text mining, drug discovery, image understanding, programming, education. Our survey delineates current strengths limitations this chatbot offers insights into potential avenues for future developments.

Language: Английский

Citations

9
BGMDB: A curated database linking gut microbiota dysbiosis to brain disorders DOI Creative Commons
Kai Shi,

Qinghua He,

Pengyang Zhao

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 879 - 886

Published: Jan. 1, 2025

Language: Английский

Citations

0
Harnessing Microalgae: Pioneering Strategies for Cost-Effective EPA Synthesis DOI
Yiting Shen, Zixu Zhang, Xin Qi

et al.

Food Bioscience, Journal Year: 2025, Volume and Issue: unknown, P. 106687 - 106687

Published: April 1, 2025

Language: Английский

Citations

0
Artificial intelligence-driven metabolic engineering is applied to the development of active ingredients in Traditional Chinese Medicine DOI Creative Commons
Guoqi Zhang, Juan Wang

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 174, P. 03013 - 03013

Published: Jan. 1, 2025

Metabolic engineering serves as a pivotal component in establishing microbial platforms for the effective biosynthesis of expensive compounds, therapeutic agents, and vegetative production systems. This field necessitates thorough comprehension intracellular biochemical networks (encompassing molecular transformation routes corresponding catalytic proteins). Nevertheless, critical catalysts that control numerous high-value target molecules have not been fully characterized, which is main bottleneck heterologous synthesis chemicals. To address this limitation, scientists devised optimized circuits through artificial biocatalysts de novo reaction sequences. With continuous accumulation biological big data, data-driven methods intelligence (AI) technology are promoting further development protein metabolic pathway design. In paper, we introduce AI-driven machine learning algorithms prediction models, also review recent research progress on AI-assisted design focusing how to use AI achieve directed evolution strains.

Language: Английский

Citations

0
Artificial Intelligence-Assisted Breeding for Plant Disease Resistance DOI Open Access
Juan Ma,

Zeqiang Cheng,

Yanyong Cao

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(11), P. 5324 - 5324

Published: June 1, 2025

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language large multi-modal model), has emerged as transformative tool enhance detection omics prediction science. This paper provides comprehensive review of AI-driven advancements detection, highlighting convolutional neural networks their linked methods through bibliometric analysis from recent research. We further discuss the groundbreaking potential models interpreting complex patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic phenomic selection by enabling high-throughput resistance-associated traits, explore AI’s role harmonizing multi-omics data predict disease-resistant phenotypes. Finally, propose some challenges future directions terms data, model, privacy facets. also provide our perspectives on integrating federated with for prediction. guide into breeding programs, facilitating translation computational advances crop

Language: Английский

Citations

0
Impact of prenatal genomics on clinical genetics practice DOI
Roni Zemet, Ignatia B. Van den Veyver

Best Practice & Research Clinical Obstetrics & Gynaecology, Journal Year: 2024, Volume and Issue: 97, P. 102545 - 102545

Published: Sept. 6, 2024

Genetic testing for prenatal diagnosis in the pre-genomic era primarily focused on detecting common fetal aneuploidies, using methods that combine maternal factors and imaging findings. The genomic era, ushered by emergence of new technologies like chromosomal microarray analysis next-generation sequencing, has transformed diagnosis. These tools enable screening a broad spectrum genetic conditions, from to monogenic disorders, significantly enhance diagnostic precision efficacy. This chapter reviews transition traditional karyotyping comprehensive sequencing-based analyses. We discuss both clinical utility challenges integrating exome genome sequencing into care underscore need ethical frameworks, improved phenotypic characterization, global collaboration further advance field.

Language: Английский

Citations

3
A Framework for Autonomous AI-Driven Drug Discovery DOI Creative Commons

Douglas W. Selinger,

Tom Wall,

Eleni Stylianou

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract The exponential increase in biomedical data offers unprecedented opportunities for drug discovery, yet overwhelms traditional analysis methods, limiting the pace of new development. Here we introduce a framework autonomous artificial intelligence (AI)-driven discovery that integrates knowledge graphs with large language models (LLMs). It is capable planning and carrying out automated programs while providing details its research strategy, progress, supporting points, enabling thorough assessment methods findings. At heart this lies “focal graph” - novel construct harnesses centrality algorithms to distill vast, noisy datasets into concise, transparent, data-driven hypotheses. By high-throughput search result interpretation, such could be used execute massive numbers searches, identify patterns across complex, diverse datasets, prioritize actionable hypotheses at scale speed unachievable by human researchers alone. We demonstrate even small- applications approach can yield novel, transparent insights relevant multiple stages process present prototype system autonomously executing multi-step target workflow. focal graph described here, automation it enables, represents promising path forward: towards deeper understanding mechanisms underlying disease true acceleration development therapeutics. Graphical

Language: Английский

Citations

0