28 research outputs found
Energy-entropy prediction of octanol–water logP of SAMPL7 N-acyl sulfonamide bioisosters
From Springer Nature via Jisc Publications RouterHistory: received 2021-03-04, accepted 2021-06-17, registration 2021-06-18, pub-print 2021-07, pub-electronic 2021-07-10, online 2021-07-10Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/L015218/1, EP/N025105/1Abstract: Partition coefficients quantify a molecule’s distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol–water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute
A foundation model for atomistic materials chemistry
Machine-learned force fields have transformed the atomistic modelling of
materials by enabling simulations of ab initio quality on unprecedented time
and length scales. However, they are currently limited by: (i) the significant
computational and human effort that must go into development and validation of
potentials for each particular system of interest; and (ii) a general lack of
transferability from one chemical system to the next. Here, using the
state-of-the-art MACE architecture we introduce a single general-purpose ML
model, trained on a public database of 150k inorganic crystals, that is capable
of running stable molecular dynamics on molecules and materials. We demonstrate
the power of the MACE-MP-0 model - and its qualitative and at times
quantitative accuracy - on a diverse set problems in the physical sciences,
including the properties of solids, liquids, gases, chemical reactions,
interfaces and even the dynamics of a small protein. The model can be applied
out of the box and as a starting or "foundation model" for any atomistic system
of interest and is thus a step towards democratising the revolution of ML force
fields by lowering the barriers to entry.Comment: 119 pages, 63 figures, 37MB PD
How to Compute Atomistic Insight in DFT clusters: the REG-IQA Approach
<p>Dataset for the paper "How to Compute Atomistic Insight in DFT clusters: the REG-IQA Approach" published to<i> Journal of Chemical Information and Modelling</i>.</p><p>The Dataset contains all the AIMAll calculations for each step of the intrinsic reaction coordinate of the HIV Protease hydrolysis described in the main manuscript and .xlsx files for all the REG-IQA results of the analyses pursued.</p>
An Interacting Quantum Atoms and Multipolar Electrostatics Study of XH…π Interactions
The interaction energies of nine XH···π
(X
= C, N, and O) benzene-containing van der Waals complexes were analyzed,
at the atomic and fragment levels, using QTAIM multipolar electrostatics
and the energy partitioning method interacting quantum atoms/fragment
(IQA/IQF). These descriptors were paired with the relative energy
gradient method, which solidifies the connection between quantum mechanical
properties and chemical interpretation. This combination provides
a precise understanding, both qualitative and quantitative, of the
nature of these interactions, which are ubiquitous in biochemical
systems. The formation of the OH···π and NH···π
systems is electrostatically driven, with the Qzz component of the quadrupole moment of the
benzene carbons interacting with the charges of X and H in XH. There
is the unexpectedly intramonomeric role of X–H (X = O, N) where
its electrostatic energy helps the formation of the complex and its
covalent energy thwarts it. However, the CH···π
interaction is governed by exchange–correlation energies, thereby
establishing a covalent character, as opposed to the literature’s
designation as a noncovalent interaction. Moreover, dispersion energy
is relevant, statically and in absolute terms, but less relevant compared
to other energy components in terms of the formation of the complex.
Multipolar electrostatics are similar across all systems
Energy-Entropy Prediction of Octanol-Water LogP of SAMPL7 N-Acyl Sulfonamide Bioisosters
Partition coefficients quantify a molecule’s distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol–water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00401-w