{"id":2064,"date":"2025-07-06T18:06:30","date_gmt":"2025-07-06T09:06:30","guid":{"rendered":"https:\/\/bmil.jnu.ac.kr\/?page_id=2064"},"modified":"2025-12-23T16:03:00","modified_gmt":"2025-12-23T07:03:00","slug":"conferences","status":"publish","type":"page","link":"https:\/\/bmil.jnu.ac.kr\/?page_id=2064","title":{"rendered":"CONFERENCES"},"content":{"rendered":"

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ADME<\/a><\/span> Artificial Intelligence<\/a><\/span> Attention mechanism<\/a><\/span> Bioinformatics<\/a><\/span> Cardiotoxicity<\/a><\/span> CYP450<\/a><\/span> DDI<\/a><\/span> Deep learning<\/a><\/span> Drugs<\/a><\/span> Ethnopharmacology<\/a><\/span> Generative model<\/a><\/span> Graph attention network<\/a><\/span> in silico<\/a><\/span> Interpretability<\/a><\/span> Knowledge graph<\/a><\/span> Machine learning<\/a><\/span> Molecular design<\/a><\/span> Natural product<\/a><\/span> Network analysis<\/a><\/span> Text mining<\/a><\/span> Transcriptome<\/a><\/span> Transformer<\/a><\/span> <\/div>
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2025<\/h3>
23.<\/div>

Junyong Park; Sunyong YooNovel Molecular Design via a Scaffold-Aware Transformer with Multi-Scale Attention Mechanisms<\/a> Conference<\/span> International<\/span><\/br>The 19th International Conference on Data and Text Mining in Biomedical Informatics, <\/span>2025<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span> | Tags: <\/span>Bioinformatics<\/a>, Generative model<\/a>, Molecular design<\/a><\/p>

@conference{Park2025b,
\r\ntitle = {Novel Molecular Design via a Scaffold-Aware Transformer with Multi-Scale Attention Mechanisms},
\r\nauthor = {Junyong Park and Sunyong Yoo},
\r\nurl = {http:\/\/bmil.jnu.ac.kr\/wp-content\/uploads\/2025\/12\/Junyoung-Park-Sunyong-Yoo-Novel-Molecular-Design-via-a-Scaffold-Aware-Transformer-with-Multi-Scale-Attention-Mechanisms.pdf},
\r\nyear  = {2025},
\r\ndate = {2025-12-17},
\r\npublisher = {The 19th International Conference on Data  and Text Mining in Biomedical Informatics},
\r\nabstract = {Recent advancements in artificial intelligence have demonstrated great potential in accelerating drug discovery by exploring vast chemical spaces and predicting molecular properties. However, conventional molecular generation models have limitations in reflecting desired molecular structures, as they often fail to incorporate specific structural constraints or target properties directly into the generation process. To overcome these limitations, we propose a novel framework that integrates a transformer-based generative model and a graph attention network-based predictive model. The generative model produces molecules with desired structural characteristics by explicitly incorporating scaffold information, while the predictive model estimates the biological activity of the generated molecules. A cyclic learning structure enables the generative and predictive models to interact iteratively, facilitating continuous evaluation and feedback during training. In addition, a multi stage tournament selection with experience memory guides the subsequent training process. Our approach accelerates the identification of scaffold-consistent, high affinity candidates by exploring novel chemical variations around a user-specified scaffold. Experimental results show that the proposed scaffold-aware transformer achieves competitive validity, uniqueness, and novelty, and effectively generates novel compounds with high predicted binding affinity for biological targets. An attention-based analysis extracts atom-level importance scores and highlights the substructures that contribute to the predicted binding affinity, providing interpretable insights into structure-activity relationships. This study provides a practical and interpretable tool for scaffold-conditioned molecular generation.},
\r\nkeywords = {Bioinformatics, Generative model, Molecular design},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Recent advancements in artificial intelligence have demonstrated great potential in accelerating drug discovery by exploring vast chemical spaces and predicting molecular properties. However, conventional molecular generation models have limitations in reflecting desired molecular structures, as they often fail to incorporate specific structural constraints or target properties directly into the generation process. To overcome these limitations, we propose a novel framework that integrates a transformer-based generative model and a graph attention network-based predictive model. The generative model produces molecules with desired structural characteristics by explicitly incorporating scaffold information, while the predictive model estimates the biological activity of the generated molecules. A cyclic learning structure enables the generative and predictive models to interact iteratively, facilitating continuous evaluation and feedback during training. In addition, a multi stage tournament selection with experience memory guides the subsequent training process. Our approach accelerates the identification of scaffold-consistent, high affinity candidates by exploring novel chemical variations around a user-specified scaffold. Experimental results show that the proposed scaffold-aware transformer achieves competitive validity, uniqueness, and novelty, and effectively generates novel compounds with high predicted binding affinity for biological targets. An attention-based analysis extracts atom-level importance scores and highlights the substructures that contribute to the predicted binding affinity, providing interpretable insights into structure-activity relationships. This study provides a practical and interpretable tool for scaffold-conditioned molecular generation.<\/div>
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