Lipophilicity Analysis Service
CD ComputaBio provides cutting-edge software-based virtual services to empower researchers, but we do not offer free software packages.
Lipophilicity is one of the fundamental properties of a potential drug, which determines its solubility in nonpolar solvents and the occurrence of various pharmacokinetic processes, including absorption, distribution, metabolism, and excretion (ADME). CD ComputaBio is committed to providing state-of-the-art lipophilicity analysis services using best-in-class computer predictive models to assist you in efficiently screening and optimizing your drug candidate.
Introduction to Lipophilicity Analysis
Lipophilicity is a fundamental property of molecules and refers to the ability of a compound to dissolve in non-polar solvents. It is usually measured by the partitioning behavior of a compound in a two-phase system such as liquid-liquid or liquid-solid, i.e., the logarithm of the octanol-water partition coefficient (log P). Lipophilicity is a property that can have a significant impact on ADME, toxicity, and pharmacological activity of a compound, so studying the lipid solubility of a test compound helps to establish the structure-activity relationship of a drug.
Fig.1 A dimensionality-reduced machine learning model for predicting single-component lipophilicity. (Kenney DH, et al.; 2023)
In the early stages of drug development, determining the lipid solubility of a test compound can help researchers identify its drugability problems and study the ADMET properties of the compound early, thereby reducing the failure rate of candidate drugs in the clinical research stage. The OECD guidelines detail log P measurements performed through different experimental protocols, such as the shake flask method and the slow stirring method. However, these traditional procedures are time-consuming, limited to extremely pure compounds, and require specific reagents. Computer prediction methods provide a promising alternative. By exploiting the large amount of information encoded in the molecular structure, these methods provide an effective way to estimate the lipophilicity of a compound without laboratory experiments.
Applications of Lipophilicity Analysis
- Understand and optimize the interaction of drug molecules with target proteins.
- Identify lipophilic and hydrophobic regions on molecules to optimize drug molecules.
- Rapidly evaluate the potential pharmacological properties of a large number of compounds.
- Predict the behavior of molecules in the body, including absorption, distribution, and metabolism.
- Compare the lipophilicity of different compounds to screen for excellent candidate compounds.
Our Services
At CD ComputaBio, our scientists use various online tools and free software to predict the lipophilicity of your molecules of interest, including ILOGP, XLOGP3, WLOGP, ALOGPS, MLOGP, and SILICOS-IT. Furthermore, we also use pkCMS and SwissADME software to determine ADME parameters to assist you in efficiently screening and optimizing your drug candidates.
Our scientists are able to interpret the lipophilicity of a wide range of molecule types, including but not limited to:
- Small Molecule Lipophilicity Analysis
- Peptide Lipophilicity Analysis
- Protein Lipophilicity Analysis
- Enzyme Lipophilicity Analysis
- Nucleic Acid Lipophilicity Analysis
- Antibody Lipophilicity Analysis
- Carbohydrate Lipophilicity Analysis
- More
Key Tools for Lipophilicity Analysis
ILOGP
- Trained using large-scale chemical molecular datasets, providing high prediction accuracy.
- Provide results in a shorter time, suitable for early drug development screening.
- Applied to batch predictions of a large number of compounds, helping to quickly screen suitable candidate molecules in the early stages of drug development.
- Lipophilicity data can be obtained through software and online tools without complex experiments.
SwissADME
- A platform that provides comprehensive predictions by integrating multiple computational methods and models.
- Provides multiple models, such as iLOGP, XLOGP3, WLOGP, etc., to predict the lipophilicity of compounds.
- Prediction of gastrointestinal absorption, blood-brain barrier permeability, and liver metabolic enzyme (CYP450) inhibition.
- Supports input of compound structures through multiple input methods such as SMILES and molecular formula, and can be parsed and calculated immediately.
Molecular Lipophilicity Potential (MLP)
- A method for calculating the lipophilicity characteristics of different regions in a molecule by analyzing its structure.
- Provides a spatial mapping of lipophilicity across the surface or volume of a molecule. These are generated based on molecular force fields, physicochemical parameters, and computer modeling software.
- Assist you in interpreting the distribution characteristics of lipophilicity in the three-dimensional space of your target molecule using MLP technology based on computation and visualization.
Delivery Results
- A report is used to present the calculation results, research results, and possible interpretation scenarios of lipophilicity and ADME.
- Characterizing the lipophilicity distribution of your molecule of interest in three dimensions, if required.
- Raw computational and simulation data for further research and analysis by the client.
- Data graphs and live simulation animations tailored to the client's specific needs.
Thanks to the cutting-edge computational analysis platform, CD ComputaBio aims to provide first-class lipophilicity analysis services for your research. Our lipophilicity analysis service not only helps you predict the lipophilicity of compounds but also quickly screen out excellent candidate molecules from a large number of compounds. Please feel free to contact us, if you are interested in our services.
References:
- Kenney DH, et al. Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients. J Cheminform. 2023;15(1):9.
- Daina A, et al. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.
* For Research Use Only.
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