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Predicting Molecular Properties

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Posted May 31, 2019
Atomic nucleus. Image credit: Marekich

Atomic nucleus. Image credit: Marekich/Wikipedia/CC BY-SA 3.0

Think you can use your data science smarts to make big predictions at a molecular level?

This challenge aims to predict interactions between atoms. Imaging technologies like MRI enable us to see and understand the molecular composition of tissues. Nuclear Magnetic Resonance (NMR) is a closely related technology which uses the same principles to understand the structure and dynamics of proteins and molecules.

Your Challenge

In this competition, you will develop an algorithm that can predict the magnetic interaction between two atoms in a molecule (i.e., the scalar coupling constant).

About Scalar Coupling

Using NMR to gain insight into a molecule’s structure and dynamics depends on the ability to accurately predict so-called “scalar couplings”. These are effectively the magnetic interactions between a pair of atoms. The strength of this magnetic interaction depends on intervening electrons and chemical bonds that make up a molecule’s three-dimensional structure.

Using state-of-the-art methods from quantum mechanics, it is possible to accurately calculate scalar coupling constants given only a 3D molecular structure as input. However, these quantum mechanics calculations are extremely expensive (days or weeks per molecule), and therefore have limited applicability in day-to-day workflows.

A fast and reliable method to predict these interactions will allow medicinal chemists to gain structural insights faster and cheaper, enabling scientists to understand how the 3D chemical structure of a molecule affects its properties and behavior.

Ultimately, such tools will enable researchers to make progress in a range of important problems, like designing molecules to carry out specific cellular tasks, or designing better drug molecules to fight disease.

Final submission deadline are due August 28, 2019 at 11:59 PM UTC.

Source: Kaggle

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