Sequential EXtreme Gradient Boosting-Based Descriptor Reduction for Size Prediction of Zwitterionic Polymer-Based Nanoparticles.

ABSTRACT: An approach based on machine learning (ML) is applied to investigate how structural descriptors affect the size of zwitterionic polymers (ZP) used in drug delivery systems (DDSs). This study examined the structural characteristics of components of ZP-based DDSs to determine how they contribute to the self-assembly phenomenon and, consequently, the final size of such DDSs. A new data set was curated through an extensive literature review, incorporating a wide range of descriptors related to molecular structures and pH. With each molecule initially characterized by 312 descriptors, reducing this extensive set to a manageable number emerged as a crucial step in the research process. To tackle this challenge, we introduced an innovative descriptor reduction strategy based on eXtreme Gradient B

SUBMITTER: Rezvantalab S 

PROVIDER: S-EPMC12355240 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature