Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050

Abstract

In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

Figure 1

Molecular dynamics simulations and artificial intelligence in scientific publishing. Data collected from Scopus as of December 21, 2023. The search was based on finding occurrences of “molecular dynamics” either alone (“MD”) or in combination with “artificial intelligence”, “machine learning”, “neural network”, and “deep learning” phrases (“MD + AI”) within the title, abstract, or keywords of publications indexed in the Scopus database. The collected data were fitted and extrapolated to the year 2050. The projected values, , are shown in the inlet rectangles and compared with publication data from the years 1998 and 2023 (N₁₉₉₈ and N₂₀₂₃, respectively). The utilized Scopus data set is available on Zenodo under DOI: 10.5281/zenodo.10673694.

Figure 2

Possible schematic interface of a futuristic molecular dynamics AI-maintained engine capable of setting up, running, and analyzing simulations. (A) Initial user-interaction interface. (B) Dashboard of molecular dynamics simulations of mammalian cells. (C) New molecule parametrization interface: adding an unlisted peptide. (D) Interface of a membrane model selection compatible with a new peptide model followed by simulation and analysis interfaces.

Figure 3

Examples of databases and applications that store and organize molecular dynamics data. (A) MDverse data explorer (https://mdverse.streamlit.app/): an online application navigating through the simulation data collected from Zenodo (https://zenodo.org/), Figshare (https://figshare.com/), and Open Science Framework (https://osf.io/) repositories. (B) NMRlipids databank (https://databank.nmrlipids.fi/): a catalog containing atomistic MD simulations of biologically relevant lipid membranes. The search result for “POPC” is shown. (C) BioExcel-CV19 (https://bioexcel-cv19.bsc.es/): a platform designed to provide web access to atomistic MD trajectories for macromolecules involved in the COVID-19 disease.