Fetching the paper…
Reading the bibliography…
Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs.
R. W. Zwanzig, “High-temperature equation of state by a perturbation method. i. nonpolar gases,” J. Chem. Phys. 22
1954
Earlier work this paper cites.
C. H. Bennett, “Efficient estimation of free energy differences from monte carlo data,” J. Comput. Phys. 22
1976
Earlier work this paper cites.
A. Klamt and G. Schüürmann, “Cosmo: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient,” J. Chem. Soc., Perkin Trans. 2 , 799–805 (1993)
1993
Earlier work this paper cites.
T. A. Halgren and R. B. Nachbar, “Merck molecular force field. iv. conformational energies and geometries for mmff94,” J. Comp. Chem. 17
1996
Earlier work this paper cites.
B. Roux and T. Simonson, “Implicit solvent models,” Biophys. Chem. 78
1999
Earlier work this paper cites.
A. Klamt and F. Eckert, “Cosmo-rs: a novel and efficient method for the a priori prediction of thermophysical data of liquids,” Fluid Phase Equilib. 172
2000
Earlier work this paper cites.
R. Zhou and B. J. Berne, “Can a continuum solvent model reproduce the free energy landscape of a β \beta -hairpin folding in water?” PNAS 99
2002
Earlier work this paper cites.
M. R. Shirts, J. W. Pitera, W. C. Swope, and V. S. Pande, “Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins,” J. Chem. Phys. 119
2003
Earlier work this paper cites.
H. Van de Waterbeemd, H. Lennernäs, and P. Artursson, Drug bioavailability: Estimation of Solubility, Permeability, Absorption and Bioavailability , Methods and principles in medicinal chemistry, Vol. 18 (Wiley-VCH Weinheim, Germany, 2004)
2004
Earlier work this paper cites.
C. Oostenbrink, A. Villa, A. E. Mark, and W. F. Van Gunsteren, “A biomolecular force field based on the free enthalpy of hydration and solvation: the gromos force-field parameter sets 53a5 and 53a6,” J. Comput. Chem. 25
2004
Earlier work this paper cites.
A. Nicholls, D. L. Mobley, J. P. Guthrie, J. D. Chodera, C. I. Bayly, M. D. Cooper, and V. S. Pande, “Predicting small-molecule solvation free energies: an informal blind test for computational chemistry,” J. Med. Chem. 51
2008
Earlier work this paper cites.
A. Onufriev, “Implicit solvent models in molecular dynamics simulations: A brief overview,” Annu. Rep. Comput. Chem. 4
2008
Earlier work this paper cites.
W. G. Noid, J.-W. Chu, G. S. Ayton, V. Krishna, S. Izvekov, G. A. Voth, A. Das, and H. C. Andersen, “The multiscale coarse-graining method. i. a rigorous bridge between atomistic and coarse-grained models,” J. Chem. Phys. 128
2008
Earlier work this paper cites.
M. Geballe, A. Skillman, A. Nicholls, J. Guthrie, and P. Taylor, “The sampl2 blind prediction challenge: introduction and overview.” J. Comput.-Aided Mol. Des. 24
2010
Earlier work this paper cites.
A. Klamt and M. Diedenhofen, “Blind prediction test of free energies of hydration with cosmo-rs,” J. Comput.-Aided Mol. Des. 24
2010
Earlier work this paper cites.
P. S. Nerenberg, B. Jo, C. So, A. Tripathy, and T. Head-Gordon, “Optimizing solute–water van der waals interactions to reproduce solvation free energies,” J. Phys. Chem. B 116
2012
Earlier work this paper cites.
S. P. Carmichael and M. S. Shell, “A new multiscale algorithm and its application to coarse-grained peptide models for self-assembly,” J. Phys. Chem. B 116
2012
Earlier work this paper cites.
J. P. Jämbeck, F. Mocci, A. P. Lyubartsev, and A. Laaksonen, “Partial atomic charges and their impact on the free energy of solvation,” J. Comp. Chem. 34
2013
Earlier work this paper cites.
J. P. Guthrie, “Sampl4, a blind challenge for computational solvation free energies: the compounds considered,” J. Comput.-Aided Mol. Des. 28
2014
Earlier work this paper cites.
D. L. Mobley and J. P. Guthrie, “Freesolv: a database of experimental and calculated hydration free energies, with input files,” J. Comput.-Aided Mol. Des. 28
2014
Earlier work this paper cites.
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, “Quantum chemistry structures and properties of 134 kilo molecules,” Sci. Data 1
2014
Earlier work this paper cites.
J. Reinisch and A. Klamt, “Prediction of free energies of hydration with cosmo-rs on the sampl4 data set.” J Comput Aided Mol Des 28
2014
Earlier work this paper cites.
E. L. Ratkova, D. S. Palmer, and M. V. Fedorov, “Solvation thermodynamics of organic molecules by the molecular integral equation theory: approaching chemical accuracy,” Chem. Rev. 115
2015
Earlier work this paper cites.
A. Klamt and M. Diedenhofen, “Calculation of solvation free energies with dcosmo-rs,” J. Phys. Chem. A 119
2015
Earlier work this paper cites.
A. Cumberworth, J. M. Bui, and J. Gsponer, “Free energies of solvation in the context of protein folding: Implications for implicit and explicit solvent models,” J. Comp. Chem. 37
2016
Earlier work this paper cites.
G. Duarte Ramos Matos, D. Y. Kyu, H. H. Loeffler, J. D. Chodera, M. R. Shirts, and D. L. Mobley, “Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the freesolv database,” J. Chem. Eng. Data 62
2017
Earlier work this paper cites.
S. Riniker, “Molecular dynamics fingerprints (mdfp): Machine learning from md data to predict free-energy differences.” J. Chem. Inf. Model. 57
2017
Earlier work this paper cites.
J. Zhang, H. Zhang, T. Wu, Q. Wang, and D. van der Spoel, “Comparison of implicit and explicit solvent models for the calculation of solvation free energy in organic solvents,” J. Chem. Theory Comput. 13
2017
Earlier work this paper cites.
M. Brieg, J. Setzler, S. Albert, and W. Wenzel, “Generalized born implicit solvent models for small molecule hydration free energies,” Phys. Chem. Chem. Phys. 19
2017
Earlier work this paper cites.
J. S. Smith, O. Isayev, and A. E. Roitberg, “Ani-1, a data set of 20 million calculated off-equilibrium conformations for organic molecules,” Sci. Data 4
2017
Cited alongside, same era.
J. Huang, S. Rauscher, G. Nawrocki, T. Ran, M. Feig, B. L. De Groot, H. Grubmüller, and A. D. MacKerell, “Charmm36m: an improved force field for folded and intrinsically disordered proteins,” Nat. Methods 14
2017
Cited alongside, same era.
S. Kashefolgheta and A. V. Verde, “Developing force fields when experimental data is sparse: Amber/gaff-compatible parameters for inorganic and alkyl oxoanions,” Phys. Chem. Chem. Phys. 19
2017
Cited alongside, same era.
Z. Wu, B. Ramsundar, E. Feinberg, J. Gomes, C. Geniesse, A. Pappu, K. Leswing, and V. Pande, “Moleculenet: a benchmark for molecular machine learning.” Chem. Sci. 9
2018
Cited alongside, same era.
M. J. Fossat, X. Zeng, and R. V. Pappu, “Uncovering differences in hydration free energies and structures for model compound mimics of charged side chains of amino acids,” J. Phys. Chem. B 125
2021
Later among the works it cites.
S. Ehlert, M. Stahn, S. Spicher, and S. Grimme, “Robust and efficient implicit solvation model for fast semiempirical methods.” J. Chem. Theory Comput. 17
2021
Later among the works it cites.
H. Lim and Y. Jung, “Mlsolva: solvation free energy prediction from pairwise atomistic interactions by machine learning.” J. Cheminform. 13
2021
Later among the works it cites.
Y. Pathak, S. Mehta, and U. Priyakumar, “Learning atomic interactions through solvation free energy prediction using graph neural networks.” J. Chem. Inf. Model. 61
2021
Later among the works it cites.
A. Alibakhshi and B. Hartke, “Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model,” Nat. Commun. 12
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Boulanger, L. Huang, C. Rupakheti, A. D. MacKerell Jr, and B. Roux, “Optimized lennard-jones parameters for druglike small molecules,” J. Chem. Theory Comput. 14
2018
Cited alongside, same era.
R. Zubatyuk, J. S. Smith, J. Leszczynski, and O. Isayev, “Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network,” Sci. Adv. 5
2019
Cited alongside, same era.
S. T. Hutchinson and R. Kobayashi, “Solvent-specific featurization for predicting free energies of solvation through machine learning,” J. Chem. Inf. Model. 59
2019
Cited alongside, same era.
H. Lim and Y. Jung, “Delfos: deep learning model for prediction of solvation free energies in generic organic solvents.” Chem. Sci. 10
2019
Cited alongside, same era.
K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Mathea, A. Palmer, V. Settels, T. Jaakkola, K. Jensen, and R. Barzilay, “Analyzing learned molecular representations for property prediction.” J. Chem. Inf. Model. 59
2019
Cited alongside, same era.
H. Cho and I. Choi, “Enhanced deep-learning prediction of molecular properties via augmentation of bond topology.” ChemMedChem 14
2019
Cited alongside, same era.
A. Cesari, S. Bottaro, K. Lindorff-Larsen, P. Banás, J. Šponer, and G. Bussi, “Fitting corrections to an rna force field using experimental data,” J. Chem. Theory Comput. 15
2019
Cited alongside, same era.
W. Wang and R. Gómez-Bombarelli, “Coarse-graining auto-encoders for molecular dynamics,” npj Comput. Mater. 5
2019
Cited alongside, same era.
2021
Later among the works it cites.
J. Weinreich, N. Browning, and O. von Lilienfeld, “Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation.” J. Chem. Phys. 154
2021
Later among the works it cites.
P. Friederich, F. Häse, J. Proppe, and A. Aspuru-Guzik, “Machine-learned potentials for next-generation matter simulations.” Nat. Mater. 20
2021
Later among the works it cites.
S. Thaler and J. Zavadlav, “Learning neural network potentials from experimental data via differentiable trajectory reweighting,” Nat. Commun. 12
2021
Later among the works it cites.
P. R. Vlachas, J. Zavadlav, M. Praprotnik, and P. Koumoutsakos, “Accelerated simulations of molecular systems through learning of effective dynamics,” J. Chem. Theory Comput. 18
2021
Later among the works it cites.
J. Wang, N. Charron, B. Husic, S. Olsson, F. Noé, and C. Clementi, “Multi-body effects in a coarse-grained protein force field,” J. Chem. Phys. 154
2021
Later among the works it cites.
J. Hoja, L. Medrano Sandonas, B. G. Ernst, A. Vazquez-Mayagoitia, R. A. DiStasio Jr, and A. Tkatchenko, “Qm7-x, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Sci. Data 8
2021
Later among the works it cites.
O. T. Unke, S. Chmiela, M. Gastegger, K. T. Schütt, H. E. Sauceda, and K.-R. Müller, “Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects,” Nat. Commun. 12
2021
Later among the works it cites.
K. Low, M. Coote, and E. Izgorodina, “Explainable solvation free energy prediction combining graph neural networks with chemical intuition.” J. Chem. Inf. Model. 62
2022
Later among the works it cites.
D. Zhang, S. Xia, and Y. Zhang, “Accurate prediction of aqueous free solvation energies using 3d atomic feature-based graph neural network with transfer learning.” J. Chem. Inf. Model. 62
2022
Later among the works it cites.
S. Thaler, M. Stupp, and J. Zavadlav, “Deep coarse-grained potentials via relative entropy minimization,” J. Chem. Phys. 157
2022
Later among the works it cites.
M. D. Polêto and J. A. Lemkul, “Integration of experimental data and use of automated fitting methods in developing protein force fields,” Commun. Chem. 5
2022
Later among the works it cites.
S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, “E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” Nat. Commun. 13
2022
Later among the works it cites.
Z. Zhang, D. Peng, L. Liu, L. Shen, and W. Fang, “Machine learning prediction of hydration free energy with physically inspired descriptors.” J. Phys. Chem. Lett. 14
2023
Later among the works it cites.
J. Karwounopoulos, Å. Kaupang, M. Wieder, and S. Boresch, “Calculations of absolute solvation free energies with transformato - application to the freesolv database using the cgenff force field,” J. Chem. Theory Comput. 19
2023
Later among the works it cites.
A. Coste, E. Slejko, J. Zavadlav, and M. Praprotnik, “Developing an implicit solvation machine learning model for molecular simulations of ionic media,” J. Chem. Theory Comput. 20
2023
Later among the works it cites.
S. Yao, R. Van, X. Pan, J. Park, Y. Mao, J. Pu, Y. Mei, and Y. Shao, “Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.” RSC Adv. 13
2023
Later among the works it cites.
A. Durumeric, N. Charron, C. Templeton, F. Musil, K. Bonneau, A. Pasos-Trejo, Y. Chen, A. Kelkar, F. Noé, and C. Clementi, “Machine learned coarse-grained protein force-fields: Are we there yet,” Curr. Opin. Struct. Biol. 79
2023
Later among the works it cites.
A. A. Duval, V. Schmidt, A. Hernández-Garcıa, S. Miret, F. D. Malliaros, Y. Bengio, and D. Rolnick, “Faenet: Frame averaging equivariant gnn for materials modeling,” in International Conference on Machine Learning (PMLR, 2023) pp. 9013–9033
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Röcken and J. Zavadlav, “Accurate machine learning force fields via experimental and simulation data fusion,” npj Comput. Mater. 10
2024
Closest in time.
X. Ding, “Optimizing force fields with experimental data using ensemble reweighting and potential contrasting,” J. Phys. Chem. B 128
2024
Closest in time.
2024
Closest in time.
P. Katzberger and S. Riniker, “A general graph neural network based implicit solvation model for organic molecules in water,” Chem. Sci. , – (2024)
2024
Closest in time.