Machine learning heralding a new development phase in molecular dynamics simulations

Abstract

Molecular dynamics (MD) simulations are a key computational chemistry technique that provide dynamic insight into the underlying atomic-level processes in the system under study. These insights not only improve our understanding of the molecular world, but also aid in the design of experiments and targeted interventions. Currently, MD is associated with several limitations, the most important of which are: insufficient sampling, inadequate accuracy of the atomistic models, and challenges with proper analysis and interpretation of the obtained trajectories. Although numerous efforts have been made to address these limitations, more effective solutions are still needed. The recent development of artificial intelligence, particularly machine learning (ML), offers exciting opportunities to address the challenges of MD. In this review we aim to familiarize readers with the basics of MD while highlighting its limitations. The main focus is on exploring the integration of deep learning with MD simulations. The advancements made by ML are systematically outlined, including the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. Additionally, the challenges and implications associated with the integration of ML and artificial intelligence are discussed. While the potential of ML-MD fusion is clearly established, further applications are needed to confirm its superiority over traditional methods. This comprehensive overview of the new perspectives of MD, which ML has opened up, serves as a gentle introduction to the exciting phase of MD development.

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

“…The task of effectively analyzing this wealth of information to generate valuable insights into the mechanisms of complex and dynamic systems such as GPCRs is particularly challenging. In this context, the synergy of MD with ML becomes crucial [ 34 , 35 , 36 ]. By integrating the high-resolution temporal and spatial data from MD simulations with the advanced pattern recognition and predictive capabilities of ML, researchers can not only process and analyze large datasets more effectively, but also uncover and predict complex molecular interactions and their biological implications with unprecedented accuracy.…”

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

“…Artificial intelligence (AI) provides an alternative approach for accelerating the generation of protein conformational ensembles. Novel machine learning/deep learning (ML/DL) approaches analyze simulation results to further guide conformation search with enhanced sampling techniques [19][20][21][22][23][24][25][26][27] . ML/DL optimizes coarse-grain models to speed up conformation transitions with preserved atomistic interactions 28,29 .…”

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

“…Artificial intelligence (AI) provides an alternative approach for accelerating the generation of protein conformational ensembles. Novel machine learning/deep learning (ML/DL) approaches analyze simulation results to further guide conformational search with enhanced sampling techniques [19][20][21][22][23][24][25][26][27][28] . ML/DL optimizes coarse-grain models to speed up conformation transitions with preserved atomistic interactions 29,30 .…”