Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.

ABSTRACT: Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperp

SUBMITTER: Bao Z 

PROVIDER: S-EPMC11520512 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature