# Machine Learning Implicit Solvation for Molecular Dynamics

arXiv: https://arxiv.org/abs/2106.07492 · HTML: https://mlsanity.com/paper/2106.07492/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Built on by (most cited first, 17 of 17)

- [Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations](https://mlsanity.com/paper/2210.07237/) (2022)
- [Machine Learning Coarse-Grained Potentials of Protein Thermodynamics](https://mlsanity.com/paper/2212.07492/) (2022)
- [Flow-matching -- efficient coarse-graining of molecular dynamics without forces](https://mlsanity.com/paper/2203.11167/) (2022)
- [Navigating protein landscapes with a machine-learned transferable coarse-grained model](https://mlsanity.com/paper/2310.18278/) (2023)
- [chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics](https://mlsanity.com/paper/2408.15852/) (2024)
- [Predicting solvation free energies with an implicit solvent machine learning potential](https://mlsanity.com/paper/2406.00183/) (2024)
- [Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics](https://mlsanity.com/paper/2302.07071/) (2023)
- [AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics](https://mlsanity.com/paper/2409.17852/) (2024)
- [Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials](https://mlsanity.com/paper/2510.11148/) (2025)
- [Extending machine learning model for implicit solvation to free energy calculations](https://mlsanity.com/paper/2510.20103/) (2025)
- [ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential](https://mlsanity.com/paper/2606.24983/) (2026)
- [Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation](https://mlsanity.com/paper/2302.11855/) (2023)
- [Operator Forces For Coarse-Grained Molecular Dynamics](https://mlsanity.com/paper/2506.19628/) (2025)
- [Coarse-Grained Boltzmann Generators](https://mlsanity.com/paper/2602.10637/) (2026)
- [All-atomistic Transferable Neural Potentials for Protein Solvation](https://mlsanity.com/paper/2605.14584/) (2026)
- [Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy](https://mlsanity.com/paper/2607.10887/) (2026)
- [Solvating machine-learning interatomic potentials for solution-phase chemistry](https://mlsanity.com/paper/2610.17226/) (2026)

## Abstract (arXiv)

Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models, as the many-body effects of the neglected solvent molecules is difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML--CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to MD simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely-used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.