Mohan Chen

Nov 6, 2024 | nature.com | Bo Chen |Duo Zhang |Fei Liu |Mohan Chen |Jun Cheng |Jiayu Dai | +2 more

AbstractMolecular dynamics (MD) is an indispensable atomistic-scale computational tool widely-used in various disciplines. In the past decades, nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units (CPU/GPU), which are well-known to suffer from their intrinsic “memory wall” and “power wall” bottlenecks.

Mar 18, 2024 | link.aps.org | Liang Sun |Mohan Chen

Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine-learning-based physical-constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the abacus package.

Dec 8, 2023 | pubs.rsc.org | Ran Liu |Zheng Liu |Mohan Chen |Hang Xing

Cooperatively designed aptamer-PROTACs for spatioselective degradation of nucleocytoplasmic shuttling protein for enhanced combinational therapy† Nucleocytoplasmic shuttling proteins (NSPs) have emerged as a promising class of therapeutic targets for many diseases. However, most NSPs-based therapies largely rely on small-molecule inhibitors with limited efficacy and off-target effects.

Nov 28, 2023 | pubs.rsc.org | Ran Liu |Zheng Liu |Mohan Chen |Hang Xing

Cooperatively Designed Aptamer-PROTACs for Spatioselective Degradation of Nucleocytoplasmic Shuttling Protein for Enhanced Combinational Therapy Nucleocytoplasmic shuttling proteins (NSPs) has emerged as a promising class of therapeutic targets for many diseases. However, most NSPs-based therapies largely rely on small-molecule inhibitors with limited efficacy and off-target effects.