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Prediction of water solubility of organic compounds using Lhasa in-house descriptors

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Water solubility is an important property for organic molecules. Poor aqueous solubility of drug candidates is one of the main obstacles in drug discovery and it affects the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of xenobiotics. Aqueous solubility also plays a role in drug formulation and in determining the persistence of organic compounds within the environment.

In this context, accurate in silico prediction of water solubility is fundamental and offers significant benefits to the drug development process within the pharmaceutical industry.
This study focuses on Quantitative Structure-Property Relationships (QSPR) for the prediction of water solubility using highly interpretable Lhasa in-house descriptors.

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