Experimental and Theoretical Screening for Green Solvents Improving Sulfamethizole Solubility.

ABSTRACT: Solubility enhancement of poorly soluble active pharmaceutical ingredients is of crucial importance for drug development and processing. Extensive experimental screening is limited due to the vast number of potential solvent combinations. Hence, theoretical models can offer valuable hints for guiding experiments aimed at providing solubility data. In this paper, we explore the possibility of applying quantum-chemistry-derived molecular descriptors, adequate for development of an ensemble of neural networks model (ENNM), for solubility computations of sulfamethizole (SMT) in neat and aqueous binary solvent mixtures. The machine learning procedure utilized information encoded in σ-potential profiles computed using the COSMO-RS approach. The resulting nonlinear model is accurate in bac

SUBMITTER: Cysewski P 

PROVIDER: S-EPMC8539550 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature