Kaspersky Lab software deemed threat to Lithuanian national security

In a strange development, Lithuania has said that software from Kaspersky Lab is a threat to its national security and hence they will not be using any of the security software developed by the Moscow-based company on computers that hold sensitive information.

The Lithuanian government said it will be removing Kaspersky Lab software from all computers that are on critical infrastructure such as energy, finance or transport including those run by private companies. Government agencies can only continue running it if their computers are not deemed sensitive by the cyber-security agency.

While Kaspersky Lab has already faced such a ban in one of the largest markets in the world – US – the latest ban in Lithuania is a blow none the less. Kaspersky’s antivirus software was banned from US government networks this year because of concerns the company has close ties to intelligence agencies in Moscow and that its software could be used to enable Russian spying.

Antivirus software made by Russian companies are already under the radar in Britain as well with Britain’s main cyber-security agency sending out a warning to the British government agencies to avoid using antivirus software from Russian companies.

The Lithuanian government said in a statement Kaspersky Labs software was “a potential threat to… national security”.

“Information from computers using the software can leak into countries where we don’t want it to end up,” Rytis Rainys, deputy director at the state cyber-security agency told Reuters. “We drew on various sources for the conclusion, including information from our partners and intelligence sources.”

Kaspersky Labs was not immediately available for a comment.

The company has repeatedly denied it has ties to any government and said it would not help a government with cyberespionage. It also says it is a scapegoat given tension between Washington and Moscow.

Machine learning could facilitate faster drug discovery

A high-precision machine learning model could help accelerate drug discovery, scientists have showed through a study published in Science Advances.

The new model can accurately predict the interactions between a protein and a drug molecule based on a handful of reference experiments or simulations. The model is so fast that it only requires a few training references and once trained, it can predict whether or not a candidate drug molecule will bind to a target protein with 99{c0986b2a9275d76b7a0ba973f056fa63aa4a0690ad4d1155b300910433c7dd97} accuracy.

This is equivalent to predicting with near-certainty the activity of hundreds of compounds after actually testing them – by running only a couple dozen tests. The new method could accelerate the screening of candidate molecules thousands of times over.

The algorithm can also tackle materials-science problems such as modelling the subtle properties of silicon surfaces, and promises to revolutionise materials and chemical modelling – giving insight into the nature of intermolecular forces.

 

The design of this algorithm, which combines local information from the neighbourhood of each atom in a structure, makes it applicable across many different classes of chemical, materials science, and biochemical problems.

The approach is remarkably successful in predicting the stability of organic molecules, as well as the subtle energy balance governing the silicon structures crucial for microelectronic applications, and does so at a tiny fraction of the computational effort involved in a quantum mechanical calculation.

The research illustrates how chemical and materials discovery is now benefitting from the Machine Learning and Artificial Intelligence approaches that already underlie technologies from self-driving cars to go-playing bots and automated medical diagnostics.

New algorithms allow us to predict the behaviour of new materials and molecules with great accuracy and little computational effort, saving time and money in the process.