Machine learning-driven insights into retention mechanism in IAM chromatography of anticancer sulfonamides: Implications for biological efficacy.

Machine learning (ML) tools offer new opportunities in drug discovery, especially for enhancing our understanding of molecular interactions with biological systems. This study develops a comprehensive quantitative structure-retention relationship (QSRR) model to elucidate sulfonamides' binding mechanisms to phospholipids via immobilized artificial membrane (IAM) chromatography. Using a dataset of over 500 sulfonamide derivatives, we combined experimental IAM-HPLC data with computational molecular descriptors and ML techniques, achieving robust predictive models.

Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches.

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors.