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Valence Bond Theory02:42

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Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
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The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Updated: Feb 22, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Hybrid Computational Strategy for Predicting Complex Ligand-Metal Architectures.

Galymzhan Moldagulov1,2, Kisung Lee1, Sanzhar Nurgaliyev1

  • 1Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS), Ulsan, Republic of Korea.

Angewandte Chemie (International Ed. in English)
|February 21, 2026
PubMed
Summary

This study introduces a hybrid computational method using Machine Learning (ML) to predict metal-ligand coordination patterns. The ML algorithm, trained on the Cambridge Structural Database (CSD), accurately predicts complex coordination for diverse ligands and metals.

Keywords:
cheminformaticscoordination modesmachine learningneural networksorganometallics

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Area of Science:

  • Computational chemistry
  • Materials science
  • Chemical informatics

Background:

  • Predicting metal-ligand coordination is crucial for designing metal complexes and catalysts.
  • Ligands can exhibit numerous coordination modes, posing challenges for chemists.
  • Existing methods struggle with complex coordination patterns.

Purpose of the Study:

  • To develop a computational approach for predicting complex metal-ligand coordination patterns.
  • To create a versatile model applicable to a wide range of ligands and metals.
  • To provide an accessible tool for chemists.

Main Methods:

  • A hybrid computational approach combining Machine Learning (ML) and knowledge-based rules.
  • Training an ML algorithm on data from the Cambridge Structural Database (CSD).
  • Developing a predictive model for coordination patterns.

Main Results:

  • The ML model successfully predicts complex coordination patterns for various ligands and metals.
  • The approach handles diverse ligand types, including hemilabile, haptic, and high-denticity ligands.
  • The model is effective across different metal oxidation states.

Conclusions:

  • The developed hybrid ML approach offers a robust solution for predicting metal-ligand coordination.
  • The tool enhances the rational design of metal complexes and catalysts.
  • The algorithm is available via RDKit and a public web portal.