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<p>Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood–brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania (<a href="https://enaloscloud.novamechanics.com/EnalosWebApps/titania/">https://enaloscloud.novamechanics.com/EnalosWebApps/titania/</a>), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.</p>

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Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月23日

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  • Nikoletta-Maria Koutroumpa,
  • Maria Antoniou,
  • Dimitra-Danai Varsou,
  • Konstantinos D. Papavasileiou,
  • Nikolaos K. Sidiropoulos,
  • Christoforos Kyprianou,
  • Andreas Tsoumanis,
  • Haralambos Sarimveis,
  • Iseult Lynch,
  • Georgia Melagraki,
  • Antreas Afantitis

摘要

Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood–brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania (https://enaloscloud.novamechanics.com/EnalosWebApps/titania/), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.