AI for Drug Discovery
Generative and predictive AI for molecular design, virtual screening, and candidate prioritization.
At LSAIL, we research and develop artificial intelligence that advances the life sciences and enhances the quality of human life. Our interests span a broad spectrum of AI, ranging from predictive and generative modeling to agentic frameworks built on large language models.
Generative and predictive AI for molecular design, virtual screening, and candidate prioritization.
Biological and biomedical data arise from diverse sources and experimental platforms. We study data integration methods that connect heterogeneous datasets and enable comprehensive understanding across modalities and scales.
Life science data exist in many different forms. We develop biology-aware AI methods that incorporate biological structure and domain knowledge to build more meaningful, robust, and interpretable models for life science.
At LSAIL, we develop AI methods for drug discovery and computational biology. Our goal is to turn complex biological and biomedical data into actionable insight for understanding disease and designing better therapeutic strategies.
We study predictive and generative models, multimodal data integration, and biology-aware machine learning frameworks that reflect real biological context. Our work spans molecules, cells, patients, and the data that connect them.
If you're curious about how AI can accelerate drug discovery and change the way we understand biology — we'd love to hear from you.
LSAIL is actively recruiting passionate and curious students — graduate (M.S. / Ph.D.) and undergraduate researchers — who want to work at the frontier of AI, drug discovery, and computational biology.
Students from computer science, AI, chemistry, biology, pharmacy, statistics, mathmatics and related areas are all welcome
What you'll do here:
• Work on real, publishable problems in AI-driven drug discovery and computational biology
• Learn deep learning, bio & cheminformatics, and biology-aware modeling
Interested? Just send your CV and your interests hyunhokim@jnu.ac.kr
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