A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

Nieto‐Draghi, Carlos; Fayet, Guillaume; Creton, Benoît; Rozanska, Xavier; Rotureau, Patricia; Hemptinne, Jean-Charles de; Ungerer, Philippe; Rousseau, Bernard; Adamo, Carlo

doi:10.1021/acs.chemrev.5b00215

Cited by 128 publications

(129 citation statements)

References 729 publications

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“…The NEWTON MD simulation code has been extended to handle DPD simulations ,. We recently proposed a review of properties that can be obtained using molecular simulation tools . A non‐exhaustive list of properties should contain: the boiling point, the vapour pressure, the surface or interfacial tension, the density, the viscosity……”

Section: Computational Methods and Resourcesmentioning

confidence: 99%

“…Hereafter, the QSPR term will include these three latter acronyms. Numerous published scientific articles deal with QSPR developments, and reviews including best practices in QSPR modelling have been proposed in the literature . This section proposes to expose some methodological aspects and to share know‐how and feedback from our experiences in terms of machine learning applications for thermophysical property predictions.…”

Section: Computational Methods and Resourcesmentioning

confidence: 99%

“…Currently, about a dozen of research engineers works in this latter entity and aims at providing theoretical solutions in terms of property predictions and understanding interactions within fluids and/or nanoporous materials. The development and the use of reliable theoretical methods for the prediction of property values are of upmost importance, and understanding interactions is a key for success . To ensure strong physical basis, the department has multiscale approaches highly integrated in responses to business unit projects.…”

Section: Introductionmentioning

confidence: 99%

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Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Creton

2017

Molecular Informatics

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The objective of the present paper is to summarize chemoinformatics based research, and more precisely, the development of quantitative structure property relationships performed at IFP Energies nouvelles (IFPEN) during the last decade. A special focus is proposed on research activities performed in the "Thermodynamics and Molecular Simulation" department, i. e. the use of multiscale molecular simulation methods in responses to projects. Molecular simulation techniques can be envisaged to supplement dataset when experimental information lacks, thus the review includes a section dedicated to molecular simulation codes, development of intermolecular potentials, and some of their possible applications. Know-how and feedback from our experiences in terms of machine learning application for thermophysical property predictions are included in a section dealing with methodological aspects. The generic character of chemoinformatics is emphasized through applications in the fields of energy, transport, and environment, with illustrations for three IFPEN business units: "Transports", "Energy Resources", and "Processes". More precisely, the review focus on different challenges such as the prediction of properties for alternative fuels, the prediction of fuel compatibility with polymeric materials, the prediction of properties for surfactants usable in chemical enhanced oil recovery, and the prediction of guest-host interactions between gases and nanoporous materials in the frame of carbon dioxide capture or gas separation activities.

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Section: Computational Methods and Resourcesmentioning

confidence: 99%

Section: Computational Methods and Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Creton

2017

Molecular Informatics

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“…It is a physico-chemical key quantity to characterize and predict the behavior of such molecules [1][2][3] . The pK A governs solubility, volume of distribution and clearance of drug molecules [4][5][6][7][8] and the thermodynamics of metabolic reactions 9 .…”

Section: Introductionmentioning

confidence: 99%

Computing pK_a Values in Different Solvents by Electrostatic Transformation

Rossini

Netz

Knapp

2016

J. Chem. Theory Comput.

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We introduce a method that requires only moderate computational effort to compute pKa values of small molecules in different solvents with an average accuracy of better than 0.7 pH units. With a known pKa value in one solvent, the electrostatic transform method computes the pKa value in any other solvent if the proton solvation energy is known in both considered solvents. To apply the electrostatic transform method to a molecule, the electrostatic solvation energies of the protonated and deprotonated molecular species are computed in the two considered solvents using a dielectric continuum to describe the solvent. This is demonstrated for 30 molecules belonging to 10 different molecular families by considering 77 measured pKa values in 4 different solvents: water, acetonitrile, dimethyl sulfoxide, and methanol. The electrostatic transform method can be applied to any other solvent if the proton solvation energy is known. It is exclusively based on physicochemical principles, not using any empirical fetch factors or explicit solvent molecules, to obtain agreement with measured pKa values and is therefore ready to be generalized to other solute molecules and solvents. From the computed pKa values, we obtained relative proton solvation energies, which agree very well with the proton solvation energies computed recently by ab initio methods, and used these energies in the present study.

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“…Such predictive approaches can provide guidance in high-throughput screening for rational design [5, 6]. An important step in establishing such utility, however, is to ensure that the approaches are suitable and well-tested.…”

Section: Introductionmentioning

confidence: 99%

Partition coefficients for the SAMPL5 challenge using transfer free energies

Jones

Brooks

Wilson

2016

J Comput Aided Mol Des

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SAMPL challenges (Mobley et al. in J Comput Aided Mol Des 28:135-150, 2014; Skillman in J Comput Aided Mol Des 26:473-474, 2012; Geballe in J Comput Aided Mol Des 24:259-279, 2010; Guthrie in J Phys Chem B 113:4501-4507, 2009) provide excellent opportunities to assess theoretical approaches on new data sets with a goal of gaining greater insight towards protein and ligand modeling. In the SAMPL5 experiment, cyclohexane-water partition coefficients were determined using a vertical solvation scheme in conjunction with the SMD continuum solvent model. Several DFT functionals partnered with correlation consistent basis sets were evaluated for the prediction of the partition coefficients. The approach chosen for the competition, a B3PW91 vertical solvation scheme, yields a mean absolute deviation of 1.9 logP units and performs well at estimating the correct hydrophilicity and hydrophobicity for the full SAMPL5 molecule set.

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A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

Cited by 128 publications

References 729 publications

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Computing pK_a Values in Different Solvents by Electrostatic Transformation

Partition coefficients for the SAMPL5 challenge using transfer free energies

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A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

Cited by 128 publications

References 729 publications

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Computing pKa Values in Different Solvents by Electrostatic Transformation

Partition coefficients for the SAMPL5 challenge using transfer free energies

Contact Info

Product

Resources

About

Computing pK_a Values in Different Solvents by Electrostatic Transformation