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Related Concept Videos

Structure of Amines01:19

Structure of Amines

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The hybridized nitrogen atom in amines possesses a lone pair of electrons and is bound to three substituents with a bond angle of around 108°, which is less than the tetrahedral angle of 109.5°. However, the C–N–H bond angle is slightly larger at 112°, with a carbon–nitrogen bond length of 147 pm. This carbon–nitrogen bond length of of amines is longer than the carbon–oxygen bond of alcohols (143 pm) but shorter than alkanes’ carbon–carbon bond (154 pm). These aspects are...
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Preparation of Amines: Alkylation of Ammonia and Amines01:30

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Alkylation is one of the methods used to prepare amines. Direct alkylation of ammonia or a primary amine with an alkyl halide gives polyalkylated amines along with a quaternary ammonium salt through successive SN2 reactions. This process of making the quaternary salt through the direct alkylation method is called exhaustive alkylation.
Each alkylation step makes the nitrogen center more nucleophilic, which triggers successive alkylations until a quaternary ammonium salt is formed. Considering...
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Nomenclature of Secondary and Tertiary Amines01:12

Nomenclature of Secondary and Tertiary Amines

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The secondary and tertiary amines are derivatives of ammonia, where two and three of its hydrogens are replaced by alkyl groups, respectively. Secondary and tertiary amines can be symmetrical with identical alkyl groups attached to the nitrogen atom or unsymmetrical when more than one type of alkyl group is present. The standard nomenclature of secondary and tertiary amines is similar to the names given to the primary amines. They are generally named alkylamines. As depicted in Figure 1, for...
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Mass Analyzers: Common Types01:19

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The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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Topology-Aware Generation and Activity-Based Filtering: A Computational-Experimental Framework for Data-Scarce

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Artificial intelligence accelerates the discovery of new quaternary ammonium compounds (QACs) to combat bacterial resistance. Computational filtering significantly enhances the quality and success rate of novel antimicrobial candidates.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Antimicrobial Drug Discovery

Background:

  • Quaternary ammonium compounds (QACs) are vital antimicrobials facing rising bacterial resistance.
  • Developing novel QACs is challenging due to limited structure-activity data and generation methods.
  • Existing methods struggle to efficiently identify promising new QAC candidates.

Purpose of the Study:

  • To compare two AI-driven computational workflows for accelerating QAC discovery under data-limited conditions.
  • To evaluate the impact of predictive computational filtering on the quality of novel QAC candidates.
  • To identify novel QACs with validated antimicrobial activity against key bacterial pathogens.

Main Methods:

  • Utilized a topology-aware variational autoencoder for generating novel QAC structures.
  • Workflow 1: Direct expert evaluation of generated QACs.
  • Workflow 2: Computational filtering of candidates for predicted antimicrobial activity before expert evaluation.
  • Experimental validation of minimum inhibitory concentrations (MICs) for selected compounds.

Main Results:

  • Workflow 2 significantly improved candidate quality: synthesis-worthy compounds increased from 9% to 38%, and invalid outputs decreased from 21% to 0%.
  • Expert evaluation time constraints were met more effectively with computational prefiltering.
  • Eleven novel QACs with validated MICs ranging from 1-32 μM against four bacterial pathogens were identified.

Conclusions:

  • AI-guided generation coupled with computational prefiltering effectively navigates data-scarce chemical spaces for drug discovery.
  • This approach enhances the efficiency and success rate of identifying novel antimicrobial agents.
  • The developed workflows provide a systematic strategy for discovering QACs with potent bioactivity, addressing the challenge of antimicrobial resistance.