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Grid-Context Convolutional Model for Efficient Molecular Surface Construction from Point Clouds
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DOI：10.1021/acs.jctc.5c00266.png)
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Abstract
En 中文
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 interactions to improve the accuracy and robustness of molecular surface construction. By leveraging learnable convolutional operators in both two-dimensional (2D) and three-dimensional (3D) space, Con2SES mitigates the limitations of grid-independent methods and ensures a more continuous and accurate surface construction. Extensive evaluations on protein molecules and diverse molecular collections demonstrate that Con2SES significantly outperforms grid-independent approaches in accuracy while maintaining computational efficiency comparable to that of the fastest grid-independent method, achieving approximately 99% accuracy and a 28-fold speedup compared to the classical SES implementation in the AMBER software package. Furthermore, Con2SES has been integrated into the AMBER platform and released as an open-source tool, providing a high-performance solution for large-scale molecular surface analysis.
Keywords:
molecular surface
solvent-excluded surface
machine learning
convolutional operators
computational chemistry
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