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

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Bioinformatics

Background:

• Accurate molecular surface representation is crucial for computational chemistry applications.
• Traditional methods like solvent-accessible surface (SAS) and solvent-excluded surface (SES) face computational and adaptability challenges.
• Existing grid-independent machine learning methods lack accuracy in complex interior regions due to limited context.

Purpose of the Study:

• To introduce Con2SES, a novel framework for accurate and robust molecular surface construction.
• To address limitations of existing methods by incorporating grid-context interactions.
• To enhance the accuracy and efficiency of molecular surface representation.

Main Methods:

• Developed Con2SES, a framework utilizing learnable convolutional operators in 2D and 3D.
• Explicitly incorporated grid-context interactions for improved surface modeling.
• Evaluated performance on protein molecules and diverse molecular collections.

Main Results:

• Con2SES achieved approximately 99% accuracy, significantly outperforming grid-independent approaches.
• Demonstrated computational efficiency comparable to the fastest grid-independent methods, with a 28-fold speedup over classical SES.
• Successfully integrated into the AMBER platform as an open-source tool.

Conclusions:

• Con2SES provides a highly accurate and computationally efficient solution for molecular surface construction.
• The framework overcomes limitations of previous methods by considering grid-context interactions.
• Con2SES is a valuable open-source tool for large-scale molecular surface analysis in computational chemistry.