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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
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Isomerism in Complexes
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery.

Albert J Kooistra1,2, Márton Vass2, Ross McGuire1,3

  • 1Centre for Molecular and Biomolecular Informatics (CMBI), Radboud University Medical Center (RadboudUMC), Nijmegen, The Netherlands.

Chemmedchem
|January 17, 2018
PubMed
Summary
This summary is machine-generated.

New scientific KNIME tools and workflows integrate diverse data for drug design. These computational approaches aid in identifying drug targets and repurposing ligands for safer, more effective medicines.

Keywords:
KNIMEcheminformatics workflowsligand designligand repurposingtarget prediction

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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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Area of Science:

  • Computational chemistry
  • cheminformatics
  • bioinformatics
  • drug discovery

Background:

  • eScience technologies are crucial for managing heterogeneous protein-ligand interaction data.
  • Effective data integration is needed to build models for designing safe and efficacious medicines.

Purpose of the Study:

  • To present scientific KNIME tools and workflows for integrating chemical, pharmacological, and structural information.
  • To enable various drug design strategies, including scaffold hopping and ligand repurposing.

Main Methods:

  • Structure-based bioactivity data mapping
  • Structure-based identification of scaffold replacement strategies
  • Ligand-based target prediction
  • Protein sequence-based binding site identification and ligand repurposing
  • Structure-based pharmacophore comparison for ligand repurposing

Main Results:

  • Demonstrated the integration of diverse data types using KNIME workflows.
  • Enabled structure-based and ligand-based approaches for drug design and repurposing.
  • Facilitated customized computer-aided drug discovery (CADD) workflows.

Conclusions:

  • The presented KNIME tools and workflows effectively integrate heterogeneous data for drug discovery.
  • The modular design and use of standards allow for reusability and customization of CADD workflows.
  • These advancements support the design of efficacious and safe medicines.