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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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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.
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Noncovalent Attractions in Biomolecules02:35

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Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
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Chemical Shift: Internal References and Solvent Effects01:17

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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
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Updated: Jul 7, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Deep learning algorithms applied to computational chemistry.

Abimael Guzman-Pando1, Graciela Ramirez-Alonso2, Carlos Arzate-Quintana1

  • 1Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.

Molecular Diversity
|December 27, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models show promise in molecular sciences but have limitations. This review categorizes deep learning algorithms for computational chemistry, detailing their pros, cons, and applications.

Keywords:
Artificial IntelligentDeep learningGraph representationMolecular Design

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Area of Science:

  • Computational chemistry
  • Molecular sciences
  • Artificial intelligence in chemistry

Background:

  • Deep learning (DL) is increasingly used in molecular sciences, demonstrating high performance and generalization.
  • Existing DL models have limitations, and their comparative advantages/disadvantages are unclear for newcomers.
  • A clear understanding of DL algorithms is crucial for advancing computational chemistry.

Purpose of the Study:

  • To review deep learning algorithms applied to molecular challenges in computational chemistry.
  • To provide a comprehensive categorization of conventional and geometric deep learning models.
  • To analyze key features, open issues, and applications of these algorithms.

Main Methods:

  • Categorization of DL algorithms into conventional and geometric approaches.
  • Analysis of input descriptors, datasets, code availability, and task solutions.
  • Review of research applications, trends, and future directions in molecular algorithm design.

Main Results:

  • Detailed analysis of conventional and geometric DL algorithms, including their strengths and weaknesses.
  • Information on datasets, libraries, computational costs (GPU, time), and optimization schemes.
  • Identification of suitable algorithms for specific tasks and common data/input practices.

Conclusions:

  • The review provides a structured overview of DL in computational chemistry, aiding algorithm selection.
  • It serves as a reference for datasets, input data, and algorithm techniques.
  • Insights into benefits and open issues support the development of novel computational chemistry systems.