Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges

Dear Colleagues,

Rapid advancements in computational methods have profoundly transformed research on soft matter and biophysics. These groundbreaking methods provide innovative solutions to accelerate our understanding and optimization of complex systems. They encompass various technologies, from molecular modeling and simulations to artificial intelligence and machine learning algorithms, each playing a crucial role in addressing multiple soft matter and biophysics challenges. In recent years, physics-based computational methods have surged in areas such as predicting the structures of proteins, RNA, drug molecules, and their complexes, identifying new targets, and optimizing molecular interactions. Computational methods offer efficient alternatives and significantly enhance our understanding of complex biological systems. They enable the efficient screening of large chemical libraries, thereby reducing the time and cost associated with traditional experimental approaches.

This Topic aims to compile the latest research and reviews on physics-based computational strategies pertaining to soft matter and biophysics. We invite submissions showcasing innovative computational techniques, including but not limited to computer-aided design, molecular docking and scoring, virtual screening, and the application of various machine learning approaches in soft matter and biophysics research. By highlighting these advancements, we aim to elucidate the transformative potential of computational methods in developing novel and practical solutions.

We welcome submissions discussing the challenges and opportunities in this rapidly evolving field, particularly those interdisciplinary studies that combine computational methods with experimental validation. This Topic will provide an up-to-date overview of physics-based computational approaches in soft matter and biophysics for future research directions.

Prof. Dr. Yunjie Zhao
Dr. Jian Wang
Topic Editors