PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 6, 2026Chemical Science1 citationsOpen Access

MAPLE: A Machine-Learning Force-Field-Native Platform for Automated Reaction Modeling and Enzyme Design

View Full Paper
XWXujian WangUniversity of PittsburghZSZeyu SunUniversity of PittsburghYZYilu ZhangUniversity of Pittsburgh

Key Points

  • The aim is to enhance reaction modeling and enzyme design using machine-learning force fields.
  • Developed a machine-learning force-field-native platform
  • Applied to automated reaction modeling
  • Focused on enzyme design
  • Utilized near-quantum mechanical accuracy
  • Improved computational cost comparable to conventional force fields
  • Enhanced accuracy for biomolecular simulations

Abstract

Machine-learning force fields (MLFFs) are reshaping computational chemistry and biology by delivering near-quantum mechanical accuracy at a computational cost comparable to conventional force fields, enabling applications in biomolecular simulation, catalysis,...

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69fa98bd04f884e66b5326bahttps://doi.org/10.1039/d6sc01279e
Ask AI
Helpful
Bookmark
Share
View Full Paper