• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Tuesday, September 2, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

Calculating the “fingerprints” of molecules with artificial intelligence

Bioengineer by Bioengineer
June 14, 2022
in Chemistry
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

“Macromolecules but also quantum dots, which often consist of thousands of atoms, can hardly be calculated in advance using conventional methods such as DFT,” says PD Dr. Annika Bande at HZB. With her team she has now investigated how the computing time can be shortened by using methods from artificial intelligence.

GNN

Credit: K. Singh, A. Bande/HZB

“Macromolecules but also quantum dots, which often consist of thousands of atoms, can hardly be calculated in advance using conventional methods such as DFT,” says PD Dr. Annika Bande at HZB. With her team she has now investigated how the computing time can be shortened by using methods from artificial intelligence.

The idea: a computer programme from the group of “graphical neural networks” or GNN receives small molecules as input with the task of determining their spectral responses. In the next step, the GNN programme compares the calculated spectra with the known target spectra (DFT or experimental) and corrects the calculation path accordingly. Round after round, the result becomes better. The GNN programme thus learns on its own how to calculate spectra reliably with the help of known spectra.

“We have trained five newer GNNs and found that enormous improvements can be achieved with one of them, the SchNet model: The accuracy increases by 20% and this is done in a fraction of the computation time,” says first author Kanishka Singh. Singh participates in the HEIBRiDS graduate school and is supervised by two experts from different backgrounds: computer science expert Prof. Ulf Leser from Humboldt University Berlin and theoretical chemist Annika Bande.

“Recently developed GNN frameworks could do even better,” she says. “And the demand is very high. We therefore want to strengthen this line of research and are planning to create a new postdoctoral position for it from summer onwards as part of the Helmholtz project “eXplainable Artificial Intelligence for X-ray Absorption Spectroscopy”.”

 

Annotation:

The work was carried out within the framework of the HEIBRiDS graduate school and is being supported by the Helmholtz project “eXplainable Artificial Intelligence for X-ray Absorption Spectroscopy” (XAI-4-XAS).

The core of the project is to extend GNN, as used at HZB, to very large molecules in combination with the probabilistic analysis of molecular motifs developed at HEREON. It is used to capture only the relevant part of the configuration phase space of the molecules, which is necessary for the accurate prediction of X-ray spectra. The results of the ML predictions allow a rigorous interpretation of XAS experiments, so that characteristic parts of the spectrum of an extended material can be assigned 1:1 to its specific structural subgroups.



Journal

Journal of Chemical Theory and Computation

DOI

10.1021/acs.jctc.2c00255

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Graph Neural Networks for Learning Molecular Excitation Spectra

Article Publication Date

6-Jun-2022

COI Statement

none

Share12Tweet8Share2ShareShareShare2

Related Posts

Fluorine “Forever Chemical” in Medicines Does Not Increase Drug Reaction Risks

Fluorine “Forever Chemical” in Medicines Does Not Increase Drug Reaction Risks

September 2, 2025
Eliminating Yellow Stains on Fabric Using Blue Light: A Scientific Breakthrough

Eliminating Yellow Stains on Fabric Using Blue Light: A Scientific Breakthrough

September 2, 2025

Unraveling the Physics Behind Universal Unusual Magnetoresistance

September 2, 2025

Quantum researchers capture real-time magnetic flipping at the core of a single atom

September 2, 2025

POPULAR NEWS

  • blank

    Breakthrough in Computer Hardware Advances Solves Complex Optimization Challenges

    154 shares
    Share 62 Tweet 39
  • Molecules in Focus: Capturing the Timeless Dance of Particles

    143 shares
    Share 57 Tweet 36
  • Needlestick Injury Rates in Nurses and Students in Pakistan

    123 shares
    Share 49 Tweet 31
  • New Drug Formulation Transforms Intravenous Treatments into Rapid Injections

    117 shares
    Share 47 Tweet 29

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Global Trends and Disparities in Urinary Tumors (1990-2046)

Fluorine “Forever Chemical” in Medicines Does Not Increase Drug Reaction Risks

Scientists Reveal Link Between Gut Fungi, Human Genetics, and Disease Susceptibility

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.