LiProS: Findable, Accessible, Interoperable, and Reusable Data Simulation Workflow to Predict Accurate Lipophilicity Profiles for Small Molecules
সংরক্ষণ করুন:
| লেখক: | , , , , , |
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| বিন্যাস: | artículo original |
| প্রকাশনার তারিখ: | 2025 |
| বিবরন: | Lipophilicity is a fundamental physicochemical property widely used to evaluate key parameters in drug design, materials science, and food engineering. It plays a critical role in predicting membrane permeability, absorption, and distribution of compounds. Moreover, lipophilicity is commonly integrated into scoring functions to model biomolecular interactions and serves as an important molecular descriptor in machine learning models for property prediction and compound classification. The election of the appropriate pH-dependent lipophilicity (mathematical equation) model is important to ensure its accuracy. The incorporation of the ion apparent partition coefficient (mathematical equation) into predictions of pH-dependent lipophilicity profiles can be essential for accurately reproducing experimental results. In accordance with the principles for findable, accessible, interoperable, and reusable data to improve data management and sharing, here, we introduce LiProS, a FAIR workflow that is easily accessible through a Google Colab notebook. LiProS assists researchers in efficiently determining the appropriate pH-dependent lipophilicity profile based on the SMILES code of their molecules of interest. In addition, LiProS demonstrated its utility in the analysis of ionizable compounds within the NAPRORE-CR natural products database, enabling the identification of the most appropriate lipophilicity formalism tailored to the physicochemical characteristics of these compounds. |
| দেশ: | Kérwá |
| প্রতিষ্ঠান: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| ভাষা: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/103652 |
| অনলাইন ব্যবহার করুন: | https://hdl.handle.net/10669/103652 https://doi.org/10.1002/minf.70007 |
| মুখ্য শব্দ: | chemoinformatics hydrophobicity lipophilicity machine learning |