Virtual lab of artificial intelligence agents accelerating nanobody design against SARS-CoV-2 variants

Artificial intelligence (AI)-driven research frameworks are reshaping the boundaries of biomedical discovery. The Virtual Lab exemplifies this transformation, assembling large language model (LLM) agents into coordinated scientific teams functioning as investigators, specialists, and critics. The system autonomously designed, scored, and refined nanobody candidates against emerging SARS-CoV-2 variants, and subsequently validated them experimentally. These findings demonstrate how AI can move beyond prediction and retrieval to serve as an active collaborator in hypothesis generation, experimental design, and translational application. As technology continues to advance, the convergence of artificial intelligence and quantum computing is expected to give rise to a new era of Quantum AI enabled biomedical research. This integration will accelerate discovery speed, enhance precision, and foster interdisciplinary collaboration, opening unprecedented opportunities for data-driven innovation in the life sciences.

Authors

Institutions

Publication Details

Journal
International Journal of Biological Sciences
Published
2025-12-06
DOI
https://doi.org/10.7150/ijbs.126093
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Virtual lab of artificial intelligence agents accelerating nanobody design against SARS-CoV-2 variants

Sun‐Uk Kim, Taeho Kwon, Seon‐Kyu Kim, Hak-Jin Kim
International Journal of Biological Sciences
Artificial Intelligence in Healthcare and Education
article

Virtual lab of artificial intelligence agents accelerating nanobody design against SARS-CoV-2 variants

Sun‐Uk Kim, Taeho Kwon, Seon‐Kyu Kim, Hak-Jin Kim
article en

Abstract

Artificial intelligence (AI)-driven research frameworks are reshaping the boundaries of biomedical discovery. The Virtual Lab exemplifies this transformation, assembling large language model (LLM) agents into coordinated scientific teams functioning as investigators, specialists, and critics. The system autonomously designed, scored, and refined nanobody candidates against emerging SARS-CoV-2 variants, and subsequently validated them experimentally. These findings demonstrate how AI can move beyond prediction and retrieval to serve as an active collaborator in hypothesis generation, experimental design, and translational application. As technology continues to advance, the convergence of artificial intelligence and quantum computing is expected to give rise to a new era of Quantum AI enabled biomedical research. This integration will accelerate discovery speed, enhance precision, and foster interdisciplinary collaboration, opening unprecedented opportunities for data-driven innovation in the life sciences.

International Journal of Biological SciencesVol. 22(2)
Biocon (Switzerland) (CH), Quantum Technologies (Sweden) (SE), Korea Research Institute of Bioscience and Biotechnology (KR), Korea University of Science and Technology (KR)
National Research Foundation, Korea Research Institute of Standards and Science, Korea Research Institute of Bioscience and Biotechnology, Korea Basic Science Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 67%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Virtual lab of artificial intelligence agents accelerating nanobody design against SARS-CoV-2 variants — Sun‐Uk Kim, Taeho Kwon, et al. · International Journal of Biological Sciences (2025) | TGRS Research Map | TGRS