Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

John Vant; Shahriar Khan; Van Ngo; Dmytro Bykov; Debsindhu Bhowmik

doi:10.1016/j.aichem.2026.100125

Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

John Vant
, Shahriar Khan
, Van Ngo
, Dmytro Bykov
, Debsindhu Bhowmik

Research output: Contribution to journal › Article › peer-review

Abstract

Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac³⁺–DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CN_{_Ac_,O_w}≈1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CN_{_Ac_,O_w}≈1 and CN_{_Ac_,O_w}≈2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac³⁺–DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

Original language	English
Article number	100125
Journal	Artificial Intelligence Chemistry
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.aichem.2026.100125
State	Published - Jun 2026

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science , Office of Advanced Scientific Computing Research, United States , under contract number DE-AC05-00OR22725 . Low-dose Understanding, Cellular Insights, and Molecular Discoveries program was supported by the U.S. Department of Energy, Office of Science , Office of Biological and Environmental Research, United States , under Contract UT-Battelle, LLC-ERKPA71 . Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, United States , managed by UT-Battelle, LLC, for the U. S. Department of Energy. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/doe-public-access-plan ).

Keywords

Actinium
Aqueous solvation
Coordination chemistry
Free energy
Hybrid ML/MM
Neural network potentials
Polarization
QM/MM

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10.1016/j.aichem.2026.100125

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@article{05b886803a7f455a9edd88a196969fab,

title = "Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways",

abstract = "Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac3+–DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CNAc,Ow≈1.7 arising from a dynamic equilibrium between one- and two-water states (37.5\% and 59.9\% of frames; three waters 2.5\%). A 5 ns potential of mean force shows two low-lying basins at CNAc,Ow≈1 and CNAc,Ow≈2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac3+–DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.",

keywords = "Actinium, Aqueous solvation, Coordination chemistry, Free energy, Hybrid ML/MM, Neural network potentials, Polarization, QM/MM",

author = "John Vant and Shahriar Khan and Van Ngo and Dmytro Bykov and Debsindhu Bhowmik",

note = "Publisher Copyright: Copyright {\textcopyright} 2026. Published by Elsevier B.V.",

year = "2026",

month = jun,

doi = "10.1016/j.aichem.2026.100125",

language = "English",

volume = "4",

journal = "Artificial Intelligence Chemistry",

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T1 - Actinium–DOTA coordination in water from hybrid ML/MM

T2 - Structure, free energies, and water-exchange pathways

AU - Vant, John

AU - Khan, Shahriar

AU - Ngo, Van

AU - Bykov, Dmytro

AU - Bhowmik, Debsindhu

PY - 2026/6

Y1 - 2026/6

N2 - Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac3+–DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CNAc,Ow≈1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CNAc,Ow≈1 and CNAc,Ow≈2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac3+–DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

AB - Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac3+–DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CNAc,Ow≈1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CNAc,Ow≈1 and CNAc,Ow≈2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac3+–DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

KW - Actinium

KW - Aqueous solvation

KW - Coordination chemistry

KW - Free energy

KW - Hybrid ML/MM

KW - Neural network potentials

KW - Polarization

KW - QM/MM

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DO - 10.1016/j.aichem.2026.100125

M3 - Article

AN - SCOPUS:105039670279

SN - 2949-7477

VL - 4

JO - Artificial Intelligence Chemistry

JF - Artificial Intelligence Chemistry

IS - 1

M1 - 100125

ER -

Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

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