Implicit solvation | EPFL Graph Search

Implicit solvation (sometimes termed continuum solvation) is a method to represent solvent as a continuous medium instead of individual “explicit” solvent molecules, most often used in molecular dynamics simulations and in other applications of molecular mechanics. The method is often applied to estimate free energy of solute-solvent interactions in structural and chemical processes, such as folding or conformational transitions of proteins, DNA, RNA, and polysaccharides, association of biological macromolecules with ligands, or transport of drugs across biological membranes. The implicit solvation model is justified in liquids, where the potential of mean force can be applied to approximate the averaged behavior of many highly dynamic solvent molecules. However, the interfaces and the interiors of biological membranes or proteins can also be considered as media with specific solvation or dielectric properties. These media are not necessarily uniform, since their properties can be d

Towards a Composite Benchmark Method for the Aqueous Solvation Free Energies of Organic Compounds

Daniel David Sadowsky

Water is ubiquitous in the biosphere, and the difficulty of rigorously treating the effects of aqueous solvation currently limits the widespread use of computational methods in many areas of environmental chemistry. We propose an approach to benchmark calculations of aqueous free energies of solvation based on molecular dynamics simulation which is derived from first principles, statistical mechanically complete, and systematically improvable. The approach partitions the solvation free energy into the free energies of solvent density change, solvent cavity organization, and solvent-solute interaction, each of which is computed from first principles. A composite approach is considered for the calculation of the contributions of quantum nuclear effects to solvation free energies and the contributions of electron correlation and relativistic effects to solvent-solute interactions.

2015

Multimodel Approach to the Optical Properties of Molecular Dyes in Solution

Iurii Timrov

We introduce a multimodel approach to the simulation of the optical properties of molecular dyes in solution, whereby the effects of thermal fluctuations and of dielectric screening on the absorption spectra are accounted for by explicit and implicit solvation models, respectively. Thermal effects are treated by averaging the spectra of molecular configurations generated, by an ab initio molecular-dynamics simulation where solvent molecules are treated explicitly. Dielectric effects are then dealt with implicitly by computing the spectra upon removal of the solvent molecules and their replacement with an effective medium, in the spirit of a continuum solvation model. Our multimodel approach is validated by comparing its, predictions with those of a fully explicit-solvation simulation for cyanidin-3-glucoside (cyanin) chromophore in water. While multimodel and fully explicit-solvent spectra may differ considerably for individual configurations along the trajectory, their time averages are remarkably similar, thus providing a solid benchmark of the former and allowing us to save considerably on the computer resources needed to predict accurate absorption spectra. The power of the proposed methodology is' finally demonstrated by the excellent agreement between its predictions and the absorption spectra of cyanin measured at strong and intermediate acidity conditions.

Amer Chemical Soc2016

Applications of Artificial Intelligence to Computational Chemistry

Nicholas John Browning

The calculation of the electronic structure of chemical systems, necessitates computationally expensive approximations to the time-independent electronic Schrödinger equation in order to yield static properties in good agreement with experimental results. These methods can also be coupled with molecular dynamics, to provide a first principles description of thermodynamic properties, dynamics and chemical reactions. Evidently, the cost of the underlying electronic structure method limits the time frame over which a system can be studied, and hence, certain chemical processes may be out-of-reach using a particular method. Furthermore, when one is interested in designing new molecules with interesting properties, a systematic enumeration of an inordinately large chemical space is typically required. The combination of both expensive electronic structure calculations and large chemical spaces results in an insurmountable barrier in computational cost. The application of artificial intelligence (AI) in computational chemistry has, over the past 20 years, seen an explosion in interest and scope with respect to these two issues. Intelligent algorithms capable of efficiently sampling chemical spaces, coupled with machine learning (ML) techniques to cheapen the calculation of electronic structure evaluations, enable both rapid throughput to search for new molecules with particular properties and in the case of ML, an increase in the timescales that can be simulated via molecular dynamics. In this thesis, computer programs have been developed that enable the application of AI algorithms to chemical and biological problems. In particular, a versatile evolutionary algorithm toolbox called EVOLVE has been developed. As a first test-case study, genetic algorithms were used to efficiently sample the vast chemical sequence space of an isolated ¿-helical peptide, from which insights are gained to rationalise the stability of particular genetically optimised peptides in a variety of implicit solvent environments. Genetic algorithms were then applied to the compositional optimisation of training sets used in machine learning models of molecular properties. The resulting optimal training sets are shown to significantly reduce out-of-sample errors on all thermodynamic and electronic properties considered. Furthermore, they reveal that there are systematic trends in the distribution of these optimally-representative molecules. Inspired by the success of machine learning, an ML-enhanced multiple time step approach for performing accurate ab initio molecular dynamics was developed. Two schemes representing different force partitioning were investigated. In the first scheme, the ML method provides an estimation of the slow (high level) force components acting on a system, while in the second, the ML forces are added to the fast (low level) components and a high level ab initio method is used to correct the error induced by the ML model. In both schemes significant overall speedups are obtained with respect to standard Velocity-Verlet integration, all-the-while maintaining the accuracy of the high level ab initio method.

EPFL2019

Applications of Artificial Intelligence to Computational Chemistry

Nicholas John Browning

EPFL2019