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Sun2019 - Predictive models of aqueous solubility of organic compounds.

ABSTRACT: Kinetic aqueous solubility (μg/mL) was experimentally determined using the same SOP in over 200 NCATS drug discovery projects. A final dataset of 11780 non-redundant molecules and their associated solubility was used to train a SVM classifier. Model Type: Predictive machine learning model. Model Relevance: Predicting the aqueous solubility of a chemical compound. Model Encoded by: Pauline (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos74bo

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2404220003 | BioModels | 2024-04-22

REPOSITORIES: BioModels

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Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.

Sun Hongmao H Shah Pranav P Nguyen Kimloan K Yu Kyeong Ri KR Kerns Ed E Kabir Md M Wang Yuhong Y Xu Xin X

Bioorganic & medicinal chemistry 20190527 14

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive ...[more]

PMID: 31176566

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