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Electron density learning of non-covalent systems.

ABSTRACT: Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density ρ( r ) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying ρ( r ) in a diverse ensemble of sidechain-sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.

SUBMITTER: Fabrizio A

PROVIDER: S-EPMC6991182 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Bioisosterism is strategically used in drug design to enhance the pharmacokinetic and pharmacodynamic properties of therapeutic molecules. The average electron density (AED) tool has been used in several studies to quantify similarities among nonclassical bioisosteres of carboxylic acid. In this study, the AED tool is used to quantify the similarities among nonclassical bioisosteres of an amide group. In particular, amide-to-1,2,3-triazole bioisosterism is considered. To evaluate the AED differences exhibited by isomers of nonclassical bioisosteres, both isomers of amide (cis and trans) and 1,2,3-triazole (1,4 and 1,5 disubstituted moieity) were considered. The amide and 1,2,3-triazole bioisosteric moieties were capped with various R groups (R= methyl, hydrogen, and chloro) to account for changes in their environment. Amide-to-triazole bioisosteric substitutions were then explored in a more realistic environment, that is, within the FDA-approved anticancer imatinib drug. The AED tool effectively identified similarities between substantially different moieties, 1,2,3-triazole and amide, showing AED differences of no more than 4%. The AED tool was also proven to be useful in evaluating the contribution of various factors affecting triazole-amide bioisosterism including isomerism and changes in their environment. The AED values of each bioisostere were transferable within a maximum difference of 2.6%, irrespective of the change in environment. The 1,4- and 1,5-disubstituted isomers of 1,2,3-triazole have AED values that differ by less than unity, 0.52%. Similarly, the AED values of the cis- and trans-amide isomers differ by only 1.31%. Overall, the AED quantitative tool not only replicated experimental observations regarding similarities in bioisosteres, but also explained and quantified each contributing factor. This demonstrates the extended utility of the AED tool from nonclassical carboxylic acid bioisosteres to amide equivalents.On the contrary, electrostatic potential maps, usually used in the literature to qualitatively evaluate bioisosterism, were not similar for the 1,2,3-triazole and amide bioisosteres, under different environments. Overall, the AED tool proves to be powerful in quantitatively evaluating and predicting bioisosterism across diverse moieties considering structural and environmental variations, making it valuable in drug design.

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Electron density learning of non-covalent systems.

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