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The need to use wavefunction or density functional theory (DFT) calculations to determine electron densities has been bypassed by a machine learning model. It will allow chemists to quickly determine properties that depend on the electron density of large systems such as van der Waals forces, halogen bonding and C-H–π interactions. These non-covalent interactions can hold insight into the binding of host–guest systems or favoured enantiomers within reaction pathways where intermediates and transition states may be stabilised by subtle attractions. The electron density distribution is one of the most powerful tools at the disposal of a computational chemist. From the electron density, properties such as charges, dipoles and electrostatic interaction energies can be determined.

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.