{"id":5712,"date":"2025-07-01T13:27:18","date_gmt":"2025-07-01T04:27:18","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=5712"},"modified":"2026-02-18T10:14:38","modified_gmt":"2026-02-18T01:14:38","slug":"contents-e006","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e006\/","title":{"rendered":"Hydration Free Energy Prediction in Molecular Design Using Multi-Sigma\u00ae"},"content":{"rendered":"\n

This case study introduces the use of AIZOTH\u2019s AI analytics platform,Multi-Sigma\u00ae, to predict, analyze key factors influencing, and optimizehydration free energy, a critical property in the drug discovery process.<\/strong><\/p>\n\n\n\n

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1. <\/strong>Prediction of Hydration Free Energy<\/h2>\n\n\n\n

The AI prediction capabilities of Multi-Sigma\u00ae enable the construction of AI models that capture the
relationship between input data and output data through model training. Using this AI model, it is possible
to accurately predict hydration free energy values from new molecular descriptors.<\/p>\n\n\n

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\"1.<\/figure>\n<\/div>\n\n\n
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2. Contribution Analysis on Hydration Free Energy<\/strong><\/h2>\n\n\n\n

The Contribution analysis functionality of Multi-Sigma\u00ae<\/sup> allows the identification of molecular descriptors that positively (and negatively) contribute to hydration free energy.<\/p>\n\n\n\n

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Strong Positive Influence<\/strong>
1. SLogP: +4.12%
2. ATS4s: +2.42%
3. ATSC1s: +2.25%
4. Xp-4d: +2.04%<\/p>\n<\/div>\n\n\n\n

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Strong Positive Influence<\/strong>
1. SlogP_VSA2: -3.29%
2. ETA_dEpsilon_D: -2.72%
3. SMR_VSA3: -2.68%
4. TopoPSANO: -2.50%<\/p>\n<\/div>\n<\/div>\n\n\n

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\"2.<\/figure>\n<\/div>\n\n\n
<\/div>\n\n\n\n

3. Optimization to Minimize Hydration Free Energy<\/h2>\n\n\n\n
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The optimization functionality of Multi-Sigma\u00ae can propose combinations of molecular descriptors that
minimize hydration free energy.<\/p>\n<\/div>\n\n\n

\n
\"3.<\/figure>\n<\/div>\n\n\n

Expected Outcomes:<\/strong>
Significant Streamlining of the Drug Discovery Process
Reduction in Experimental Costs
Realization of Innovative Molecular Design
Shortening of Development Time<\/p>\n\n\n\n

Advantages of Optimization with Multi-Sigma\u00ae:<\/strong>
For instance, optimization can be conducted under the condition
that nHBDon takes only integer values. Moreover, optimization
can also be carried out by constraining the range of input values.<\/p>\n\n\n\n

<\/div>\n\n\n\n

(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.<\/p>\n\n\n\n

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Free Trial<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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Joint Research<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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Consulting<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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