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Molecular Shapes

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...

Resonance and Hybrid Structures

Resonance and Hybrid Structures

According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

High-Resolution Mass Spectrometry (HRMS)

High-Resolution Mass Spectrometry (HRMS)

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Mass Analyzers: Overview

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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...

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Related Experiment Video

Updated: Sep 12, 2025

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

How to crack a SMILES: automatic crosschecked chemical structure resolution across multiple services using

Simon Müller¹

¹Institute of Thermal Separation Processes, Hamburg University of Technology, Eißendorfer Straße 38, 21073, Hamburg, Germany. simon.mueller@tuhh.de.

Journal of Cheminformatics

|August 5, 2025

Summary

MoleculeResolver is a new Python tool that accurately resolves chemical structures from names and identifiers. It improves data quality for computational chemistry and predictive modeling by crosschecking multiple databases.

Keywords:

Chemical structure retrieval Identifier ML MoleculeResolver Python QSPR SMILES

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Last Updated: Sep 12, 2025

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Area of Science:

Computational chemistry
Cheminformatics
Data science

Background:

Accurate chemical structure resolution from textual identifiers is crucial for computational chemistry and predictive modeling.
Existing methods often suffer from inconsistencies and inaccuracies when converting chemical identifiers to canonical SMILES strings.

Purpose of the Study:

To introduce MoleculeResolver, an automated Python-based tool for robust chemical structure resolution.
To address common inaccuracies in converting chemical identifiers to canonical SMILES strings.

Main Methods:

MoleculeResolver crosschecks structures from multiple chemical databases.
It implements rigorous identifier plausibility checks and standardizes molecular structures.
A unique resolution algorithm intelligently selects the most accurate chemical representation.

Main Results:

Benchmarks demonstrate MoleculeResolver significantly enhances precision and recall.
The tool shows improved overall reliability compared to single-source methods.
MoleculeResolver proves effective across diverse chemical datasets.

Conclusions:

MoleculeResolver is a valuable resource for improving molecular data quality.
It enhances the reliability of chemical identifiers for computational analysis.
The tool supports chemists, data scientists, and researchers in predictive model development.