Grid-Context Convolutional Model for Efficient Molecular Surface Construction from Point Clouds.

ABSTRACT: Accurate and efficient molecular surface representation is essential in computational chemistry, impacting applications such as enzymology, rational drug design, and molecular recognition. Traditional approaches, including solvent-accessible surface (SAS) and solvent-excluded surface (SES), are widely used but often suffer from computational inefficiencies and limited adaptability to complex molecular structures. More recently, grid-independent machine learning methods have accelerated surface construction; however, these approaches rely solely on local single-grid information and fail to capture grid-context interactions, leading to inaccuracies in modeling complex interior surface regions. In this work, we introduce Con2SES, a novel framework that explicitly incorporates grid-context int

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12571668 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature