Research topics

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Overview

Our research focuses on the development and application of multiscale molecular simulations methods for soft-condensed-matter materials. We are particularly invested in using multiscale modeling to explore chemical compound space. The group develops methodologies to accelerate compound-space exploration by means of high-throughput molecular dynamics simulations. Transferable coarse-grained models have the capability to reduce the size of chemical space—a property we leverage to more easily navigate the thermodynamic properties of a large subset of chemical compounds. Coarse-graining also eases the identification of structure–property relationships and design rules for molecular discovery. Other activities include method development of coarse-grained models; machine learning for soft matter; non-equilibrium dynamical reweighting; force-field development; polymer, protein, and phospholipid membrane simulations.

Coarse-graining for molecular discovery

Active learning of molecular probes selective to cardiolipin

Coarse-grained (CG) molecular simulations are used for molecular discovery of small molecule probes that specifically bind to cardiolipin. We combine rigorous free-energy calculations, selectivity prediction inside a low-dimensional chemical-space embedding, and Bayesian optimization to identify physicochemical design rules. Said design rules are used to further filter a chemical vendor database. Out of 20 compounds tested, we identify three compounds selective in vitro and even one in vivo.

doititlejournalvolumeyear
10.1021/acs.jctc.3c00201Condensed-Phase Molecular Representation to Link Structure and Thermodynamics in Molecular DynamicsJournal of Chemical Theory and Computation192023
10.1039/D2CB00125JCLiB – a novel cardiolipin-binder isolated <i>via</i> data-driven and <i>in vitro</i> screeningRSC Chemical Biology32022
10.1039/D2SC00116KData-driven discovery of cardiolipin-selective small molecules by computational active learningChemical Science132022

Machine learning for soft-matter materials

Efficient learning of coarse-grained molecular simulations

We explore the links between machine learning (ML) and multiscale molecular modeling. Emphasis is placed on tailoring ML models to problem at hand, may that be in the representation of the architecture itself. Kernel-ridge regression, used for small datasets, eases the (sometimes analytical) tailoring of physical properties. Deep neural networks offer more flexibility, expressivity.

doititlejournalvolumeyear
10.1063/5.0012230Hydration free energies from kernel-based machine learning: Compound-database biasThe Journal of Chemical Physics1532020
10.1126/sciadv.aaz4301Designing exceptional gas-separation polymer membranes using machine learningScience Advances62020
10.1021/acs.jctc.9b01256Kernel-Based Machine Learning for Efficient Simulations of Molecular LiquidsJournal of Chemical Theory and Computation162020
10.1088/2632-2153/ab80b7Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencodersMachine Learning: Science and Technology12020

Non-equilibrium dynamical reweighting

MaxCal for ron-equilbrium reweighting

We extend the concept of statistical reweighting to non-equilibrium steady states. We use stochastic thermodynamics to compute the entropy production along microtrajectories and reweigh them using a Maximum Caliber approach. Extension to collective variables is presented.

doititlejournalvolumeyear
10.1063/5.0042972Reweighting non-equilibrium steady-state dynamics along collective variablesThe Journal of Chemical Physics1542021
10.1103/PhysRevE.100.060103Microscopic reweighting for nonequilibrium steady-state dynamicsPhysical Review E1002019

Deep backmapping

Deep backmapping of coarse-grained configurations

We generate condensed molecular structures as a refinement—backmapping—of a coarse-grained structure. We apply generative adversarial networks (GAN) conditional on the coarse-grained configuration to upscale to atomistic structure. Sampled configurations accurately target the atomistic Boltzmann distribution, and do not require pre-equilibration steps. Remarkable transferability is found across temperature for a polymer melt. Applications to chemical transferability are also investigated.

doititlejournalvolumeyear
10.3389/fchem.2022.982757Benchmarking coarse-grained models of organic semiconductors via deep backmappingFrontiers in Chemistry102022
10.1063/5.0039102Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferabilityAPL Materials92021
10.1088/2632-2153/abb6d4Adversarial reverse mapping of equilibrated condensed-phase molecular structuresMachine Learning: Science and Technology12020

Kinetic properties of coarse-grained models

Biased Markov state model

We analyze and improve the kinetic properties of coarse-grained simulations. Methodologies include Markov state models biased with external information; Conformational surface hopping for improved reconstruction of the potential of mean force.

doititlejournalvolumeyear
10.1063/5.0031249Coarse-grained conformational surface hopping: Methodology and transferabilityThe Journal of Chemical Physics1532020
10.1063/1.5064808Automated detection of many-particle solvation states for accurate characterizations of diffusion kineticsThe Journal of Chemical Physics1502019
10.1103/physrevlett.121.256002Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing DynamicsPhysical Review Letters1212018
10.1063/1.5025125Structural-kinetic-thermodynamic relationships identified from physics-based molecular simulation modelsThe Journal of Chemical Physics1482018
10.3390/computation6010021The Role of Conformational Entropy in the Determination of Structural-Kinetic Relationships for Helix-Coil TransitionsComputation62018
10.1140/epjst/e2016-60114-5Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fieldsThe European Physical Journal Special Topics2252016
10.1063/1.4941455Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external informationThe Journal of Chemical Physics1442016

High-throughput screening of thermodynamic properties

Automated Martini parametrization

We use coarse-graining (CG) to emulate a high-throughput screening experiment for free-energy calculations. We start from the CG Martini model, and tailor the bead parametrization to explore the chemical space of small molecules. Applications tailored to phopholipid membranes.

doititlejournalvolumeyear
10.1038/s41597-020-0391-0Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeationsScientific Data72020
10.1103/PhysRevE.100.033302Controlled exploration of chemical space by machine learning of coarse-grained representationsPhysical Review E1002019
10.1080/00268976.2019.1601787Revisiting the Meyer-Overton rule for drug-membrane permeabilitiesMolecular Physics1172019
10.1021/acscentsci.8b00718Drug–Membrane Permeability across Chemical SpaceACS Central Science52019
10.1063/1.4987012<i>In silico</i> screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean forceThe Journal of Chemical Physics1472017
10.1016/j.bbrc.2017.08.095Efficient potential of mean force calculation from multiscale simulations: Solute insertion in a lipid membraneBiochemical and Biophysical Research Communications4982017

Machine learning of non-covalent interactions

Van der Waals interactions

We combine physics-based potentials and machine learning for accurate and transferable non-covalent interactions. Physics-based models include atomic multipoles and many-body dispersion. The coefficients are learned by kernel-ridge regression across conformations and composition of small organic molecules.

doititlejournalvolumeyear
10.1063/1.5009502Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learningThe Journal of Chemical Physics1482018
10.1021/acs.jctc.5b00301Transferable Atomic Multipole Machine Learning Models for Small Organic MoleculesJournal of Chemical Theory and Computation112015
10.1063/1.4885339Toward transferable interatomic van der Waals interactions without electrons: The role of multipole electrostatics and many-body dispersionThe Journal of Chemical Physics1412014

Static atomic multipole electrostatics in condensed-phase simulations

Force propagation on electrostatic multipoles

Static atomic multipoles provide an improved description of the electrostatic potential of the system. Molecular dynamics simulations of condensed-phase systems demonstrate enhanced equilibrium and dynamical quantities: e.g., free energy of hydration and 2D infra-red spectroscopy.

doititlejournalvolumeyear
10.1021/acs.jpcb.1c05423Multipolar Force Fields for Amide-I Spectroscopy from Conformational Dynamics of the Alanine TrimerThe Journal of Physical Chemistry B1252021
10.1021/acs.jctc.6b00202Impact of Quadrupolar Electrostatics on Atoms Adjacent to the Sigma-Hole in Condensed-Phase SimulationsJournal of Chemical Theory and Computation122016
10.1063/1.4916630Solvation of fluoro-acetonitrile in water by 2D-IR spectroscopy: A combined experimental-computational studyThe Journal of Chemical Physics1422015
10.1021/jp508052qMultipolar Force Fields and Their Effects on Solvent Dynamics around Simple SolutesThe Journal of Physical Chemistry B1192015
10.1021/jp5011692Computational Two-Dimensional Infrared Spectroscopy without Maps:<i>N</i>-Methylacetamide in WaterThe Journal of Physical Chemistry B1182014
10.1021/ci400548wDeriving Static Atomic Multipoles from the Electrostatic PotentialJournal of Chemical Information and Modeling532013
10.1021/ct400803fLeveraging Symmetries of Static Atomic Multipole Electrostatics in Molecular Dynamics SimulationsJournal of Chemical Theory and Computation92013
10.1021/jp400593cScoring Multipole Electrostatics in Condensed-Phase Atomistic SimulationsThe Journal of Physical Chemistry B1172013

Structure formation in peptide coarse-graining

Coarse-grained peptide model

Using a top-down coarse-grained peptide model, we study secondary and tertiary structure formation in different environments and scenarios: alpha-helix vs. beta-sheet folding, microcanonical analysis of folding, structural alignment of capsid interfaces, and peptide-membrane interactions.

PhD thesis

doititlejournalvolumeyear
10.1021/acs.jpcb.9b10469Probing Nanoparticle/Membrane Interactions by Combining Amphiphilic Diblock Copolymer Assembly and PlasmonicsThe Journal of Physical Chemistry B1242020
10.1063/1.4935487Folding and insertion thermodynamics of the transmembrane WALP peptideThe Journal of Chemical Physics1432015
10.1007/s00232-014-9738-9Enhanced Sampling of Coarse-Grained Transmembrane-Peptide Structure Formation from Hydrogen-Bond Replica ExchangeThe Journal of Membrane Biology2482014
10.1063/1.4867465More than the sum of its parts: Coarse-grained peptide-lipid interactions from a simple cross-parametrizationThe Journal of Chemical Physics1402014
10.1021/ct200888uCoarse-Grained and Atomistic Simulations of the Salt-Stable Cowpea Chlorotic Mottle Virus (SS-CCMV) Subunit 26–49: β-Barrel Stability of the Hexamer and Pentamer GeometriesJournal of Chemical Theory and Computation82012
10.1016/j.bpj.2011.03.056Structural Basis of Folding Cooperativity in Model Proteins: Insights from a Microcanonical PerspectiveBiophysical Journal1002011
10.1021/ja105206wInterplay between Secondary and Tertiary Structure Formation in Protein Folding CooperativityJournal of the American Chemical Society1322010
10.1063/1.3152842Generic coarse-grained model for protein folding and aggregationThe Journal of Chemical Physics1302009