Molecular dynamics based descriptors for predicting supramolecular gelation

Whilst the field of supramolecular gels is rapidly moving towards complex materials and applications, their design is still an effortful and laborious trial-and-error process. Herein, we introduce four new descriptors that can be derived from all-atom molecular dynamics simulations and which are able to predict supramolecular gelation in both water and organic solvents. Their predictive ability was demonstrated via two separate machine learning techniques, a decision tree and an artificial neural network, with a dataset composed of urea-based gelators. Owing to the physical relevance of these descriptors to the supramolecular gelation process, their use could be conceptualized to other classes of supramolecular gelators and hence steer their design.

[1]  D. Adams,et al.  Low-Molecular-Weight Gels: The State of the Art , 2017 .

[2]  M. Alonso,et al.  Rationalising Supramolecular Hydrogelation of Bis-Urea-Based Gelators through a Multiscale Approach. , 2019, ChemPlusChem.

[3]  Yue Liu,et al.  Materials discovery and design using machine learning , 2017 .

[4]  Feng Lin,et al.  Machine Learning Directed Search for Ultraincompressible, Superhard Materials. , 2018, Journal of the American Chemical Society.

[5]  J. Steed,et al.  Gradual Transition from NH···Pyridyl Hydrogen Bonding to the NH···O Tape Synthon in Pyridyl Ureas , 2008 .

[6]  Xian Jun Loh,et al.  Supramolecular soft biomaterials for biomedical applications , 2014 .

[7]  K. Matyjaszewski,et al.  Mechanistically Guided Predictive Models for Ligand and Initiator Effects in Copper-Catalyzed Atom Transfer Radical Polymerization (Cu-ATRP). , 2019, Journal of the American Chemical Society.

[8]  A. Ajayaghosh,et al.  The Chemistry and Applications of π-Gels , 2016 .

[9]  Samuel L. C. Moors,et al.  Water distribution at the electrified interface of deep eutectic solvents , 2019, Nanoscale advances.

[10]  R. Ulijn,et al.  Computational prediction of tripeptide-dipeptide co-assembly , 2018, Molecular Physics.

[11]  Berk Hess,et al.  GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers , 2015 .

[12]  Rampi Ramprasad,et al.  Machine Learning Force Fields: Construction, Validation, and Outlook , 2016, 1610.02098.

[13]  P. Dastidar,et al.  Supramolecular gelling agents: can they be designed? , 2008, Chemical Society reviews.

[14]  Alexander D. MacKerell,et al.  All‐atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data , 2000 .

[15]  H. Cantow,et al.  Light scattering from polymers , 1983 .

[16]  VINCENT ZOETE,et al.  SwissParam: A fast force field generation tool for small organic molecules , 2011, J. Comput. Chem..

[17]  Chris Sander,et al.  The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies , 1995, J. Comput. Chem..

[18]  Nina Nikolova-Jeliazkova,et al.  QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review , 2005, Alternatives to laboratory animals : ATLA.

[19]  Rein V. Ulijn,et al.  Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly , 2011, The journal of physical chemistry letters.

[20]  T. Bradshaw,et al.  Insights into low molecular mass organic gelators: a focus on drug delivery and tissue engineering applications. , 2014, Soft matter.

[21]  Daisuke Shiomi,et al.  Quantum Chemistry on Quantum Computers: A Method for Preparation of Multiconfigurational Wave Functions on Quantum Computers without Performing Post-Hartree–Fock Calculations , 2018, ACS central science.

[22]  Robert P. Sheridan,et al.  Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR , 2004, J. Chem. Inf. Model..

[23]  Martin Fechner,et al.  More bang for your buck: Improved use of GPU nodes for GROMACS 2018 , 2019, J. Comput. Chem..

[24]  Anand Chandrasekaran,et al.  Solving the electronic structure problem with machine learning , 2019, npj Computational Materials.

[25]  Russ B Altman,et al.  Machine learning in chemoinformatics and drug discovery. , 2018, Drug discovery today.

[26]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[27]  Gerhard Hummer,et al.  Native contacts determine protein folding mechanisms in atomistic simulations , 2013, Proceedings of the National Academy of Sciences.

[28]  M. Parrinello,et al.  Polymorphic transitions in single crystals: A new molecular dynamics method , 1981 .

[29]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[30]  José Mario Martínez,et al.  PACKMOL: A package for building initial configurations for molecular dynamics simulations , 2009, J. Comput. Chem..

[31]  Takashi Kato,et al.  A new urea gelator: incorporation of intra- and intermolecular hydrogen bonding for stable 1D self-assembly. , 2003, Organic & biomolecular chemistry.

[32]  D. Adams,et al.  Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints , 2016, Chemical science.

[33]  P. Topham,et al.  Peptide conjugate hydrogelators , 2010 .

[34]  Christopher D. Jones,et al.  Gelation by supramolecular dimerization of mono(urea)s. , 2016, Chemical communications.

[35]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[36]  I. Tetko,et al.  Applicability domain for in silico models to achieve accuracy of experimental measurements , 2010 .

[37]  J. V. Esch.  We can design molecular gelators, but do we understand them? , 2009 .

[38]  A. Spek,et al.  Cyclic Bis‐Urea Compounds as Gelators for Organic Solvents , 1999 .

[39]  V. John,et al.  Urea and thiourea derivatives as low molecular-mass organogelators. , 2005, Chemistry.

[40]  B. Feringa,et al.  Balancing hydrogen bonding and van der Waals interactions in cyclohexane-based bisamide and bisurea organogelators. , 2009, Langmuir : the ACS journal of surfaces and colloids.

[41]  Yutao Sang,et al.  Nanoarchitectonics through supramolecular gelation: formation and switching of diverse nanostructures , 2019, Molecular Systems Design & Engineering.

[42]  L. Bouteiller,et al.  Organogel formation rationalized by Hansen solubility parameters. , 2011, Chemical communications.

[43]  L. Bouteiller,et al.  Organogel formation rationalized by Hansen solubility parameters: influence of gelator structure. , 2015, Soft matter.

[44]  Abhinav Vishnu,et al.  Deep learning for computational chemistry , 2017, J. Comput. Chem..

[45]  Matthew S. Sigman,et al.  Developing a Predictive Solubility Model for Monomeric and Oligomeric Cyclopropenium-Based Flow Battery Catholytes. , 2019, Journal of the American Chemical Society.

[46]  G. Schneider,et al.  Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. , 2019, Chemical reviews.

[47]  P. Albouy,et al.  Organogel formation rationalized by Hansen solubility parameters: improved methodology. , 2018, Soft matter.

[48]  O. Anatole von Lilienfeld,et al.  Quantum Machine Learning in Chemical Compound Space , 2018 .

[49]  B. Escuder,et al.  Supramolecular gels as active media for organic reactions and catalysis , 2010 .

[50]  Y. Akpalu.  Scattering from Polymers , 2010 .

[51]  Klaus-Robert Müller,et al.  Finding Density Functionals with Machine Learning , 2011, Physical review letters.

[52]  P. Schreiner,et al.  Computational Chemistry: The Fate of Current Methods and Future Challenges. , 2018, Angewandte Chemie.

[53]  I. Hamley,et al.  Low-molecular-weight gelators: elucidating the principles of gelation based on gelator solubility and a cooperative self-assembly model. , 2008, Journal of the American Chemical Society.

[54]  H. Ramon,et al.  Robust scalable synthesis of a bis-urea derivative forming thixotropic and cytocompatible supramolecular hydrogels. , 2019, Chemical communications.

[55]  Jonathan A. Kitchen,et al.  Supramolecular pyridyl urea gels as soft matter with antibacterial properties against MRSA and/or E. coli. , 2014, Chemical communications.

[56]  Daniela Kalafatovic,et al.  Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels. , 2015, Nature chemistry.