{"id":5696,"date":"2025-07-01T11:27:24","date_gmt":"2025-07-01T02:27:24","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=5696"},"modified":"2025-12-05T16:02:04","modified_gmt":"2025-12-05T07:02:04","slug":"contents-e005","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e005\/","title":{"rendered":"Prediction Model Integration and Multi-Objective Optimization in Pharmaceutical Development Using Multi-Sigma\u00ae"},"content":{"rendered":"\n

This case study showcases Aizoth’s Multi-Sigma\u00ae<\/sup>, leveraging its AI chain analysis and multi-objective optimization to balance drug activity (pIC50) and side effects for BACE inhibitors, potential Alzheimer\u2019s treatments<\/strong><\/p>\n\n\n\n

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1. Prediction of Drug Activity and Side Effects (Risk of Vascular Disorders)<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s prediction functionality enables training of AI models using molecular descriptors and biological properties to capture their interrelationship.<\/p>\n\n\n

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2. Contribution Analysis of Key Influences on Drug Activity and Side Effects (Risk of Vascular Disorders)<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>\u2019s contribution analysis allows the identification of molecular descriptors that contribute to drug activity and the risk of vascular disorders, respectively.<\/p>\n\n\n

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3. Optimization of Drug Activity and Side Effects (Risk of Vascular Disorders)<\/strong><\/h2>\n\n\n\n

By combining Multi-Sigma\u00ae<\/sup><\/strong>‘s AI chain analysis and optimization functionalities, it\u2019s possible to propose combinations of molecular descriptors that maximize drug activity while minimizing the risk of vascular disorders.<\/p>\n\n\n\n

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Using Multi-Sigma\u00ae<\/sup><\/strong>‘s AI chain analysis functionality, multi-objective optimization was performed.<\/p>\n\n\n\n

As a result, a solution with a drug activity (pIC50 value) of 166.2 and a vascular disorder risk of 0.062 was obtained.<\/p>\n\n\n

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(Note) The data used in this analysis is processed and edited based on the data published in the article below, under MIT license.
Dataset: Molecular data obtained from the NCI database and MoleculeNet. Molecular descriptors calculated using the Mordred module.  Also, data sourced from Kaggle (https:\/\/www.kaggle.com\/datasets\/mmelahi\/cheminformatics).<\/p>\n\n\n\n

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