# CAREER: Probing pathway complexity of biomolecular assemblies through coarse-grained deep learning models

> **NSF 01003031DB NSF RESEARCH & RELATED ACTIVIT** · Colorado School of Mines (CO) · $840,000

## Abstract

Living systems build complex structures, such as cellular scaffolds or protein-based compartments, by assembling many small pieces in dynamic and often unpredictable ways. These processes do not always follow a single path. Instead, they can proceed through many possible routes depending on environmental conditions such as temperature and chemical signals. This project seeks to determine how and why certain assembly pathways are preferred over others, especially when systems are driven away from stable states by external influences. To address this challenge, the project will develop new computational tools that combine physics-based simulations with machine learning to track how structures form over time and to quantify the “irreversibility” of different pathways. By identifying which pathways are most likely to occur, this work will enable new strategies to design biomolecular materials that respond to their environment, with applications in biotechnology, medicine, and sustainable manufacturing. These advances will contribute to national priorities in health, energy, and advanced materials by enabling predictive design of complex molecular assemblies. In parallel, the project will create interactive learning tools, including hands-on simulations and visual modules, to introduce students to computational biology and data-driven science. These educational activities will help prepare the next generation of scientists to work at the interface of biology, physics, engineering, and artificial intelligence.

This project develops a data-driven, multiscale computational framework to quantify pathway complexity during stochastic, out-of-equilibrium biomolecular assembly. The central question is whether path entropy production can serve as a unifying metric to distinguish thermodynamic versus kinetic control and to predict preferential assembly pathways under nonequilibrium conditions. To probe this question, the project integrates coarse-grained molecular simulations w

## Key facts

- **NSF award ID:** 2543548
- **Awardee organization:** Colorado School of Mines (CO)
- **SAM.gov UEI:** JW2NGMP4NMA3
- **PI:** Alexander J Pak
- **Primary program:** 01003031DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, NANOSCALE BIO CORE, Biotechnology
- **Estimated total:** $840,000
- **Funds obligated:** $672,000
- **Transaction type:** Continuing Grant
- **Period:** 06/01/2026 → 05/31/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2543548

## Citation

> US National Science Foundation, Award 2543548, CAREER: Probing pathway complexity of biomolecular assemblies through coarse-grained deep learning models. Retrieved via AI Analytics 2026-05-20 from https://api.ai-analytics.org/grant/nsf/2543548. Licensed CC0.

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