The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods toward its solution have been proposed. Recently, it has been suggested that the use of the committor, which provides a precise formal description of rare events, could be of use in this context. We have recently followed up on this suggestion and proposed a committor-based method that promotes frequent transitions between the metastable states of the system and allows extensive sampling of the process transition state ensemble. One of the strengths of our approach is being self-consistent and semiautomatic, exploiting a variational criterion to iteratively optimize a neural-network-based parametrization of the committor, which uses a set of physical descriptors as input. Here, we further automate this procedure by combining our previous method with the expressive power of graph neural networks, which can directly process atomic coordinates rather than descriptors. Besides applications on benchmark systems, we highlight the advantages of a graph-based approach in describing the role of solvent molecules in systems, such as ion pair dissociation or ligand binding.
Peilin Kang, Jintu Zhang, Enrico Trizio et al.· Journal of Chemical Theory a...· 6 citations
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations
We offer a practical and conceptual introduction to some of the current approaches to modelling enzymatic reaction mechanisms, ranging from quantum mechanics (QM), molecular mechanics (MM), and hybrid QM/MM approaches to enhanced sampling methods, knowledge-based approaches, and machine learning (ML) advances. We discuss how static and dynamic QM/MM approaches, as well as multi-PES strategies, have contributed to understanding the role of conformational diversity, electrostatic preorganization and solvent participation in the determination of catalytic barriers and reaction paths. We focus on how advanced sampling techniques and data-driven collective variables have enabled the exploration of rare events and reaction coordinates, as well as how knowledge- and rule-based approaches have facilitated the interpretation and hypothesis generation for different families of enzymes. Recent developments in ML potentials, ML collective variables, and committor-based sampling are presented as innovative methods that have been able to address some of the current challenges in accuracy, sampling efficiency, and the identification of low-dimensional representations of reaction coordinates. A case study of α-amylase demonstrates how the combination of these strategies leads to a comprehensive understanding of enzyme reactivity, from the chemical to the conformational level. Collectively, these developments contribute to a predictive understanding of enzymatic catalysis, which will have extensive implications in enzyme engineering, sustainable chemistry, and drug discovery. Advances in high performance computing, automated simulation pipelines and data formats will likely make multiscale simulation more accessible and reproducible. Simultaneously, the combined application of mechanistic knowledge, ML, and experimental validation will hopefully advance the discovery and optimization of biocatalysts with well-defined properties, tailored to meet pressing societal needs, such as plastic biodegradation, carbon sequestration, sustainable synthesis and personalised medicine.
Rui P. P. Neves, João T. S. Coimbra, Pedro Paiva et al.· Chemical Science· 0 citations