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Machine learning hits the big screen Posted on May 15 - 2018

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Deep learning algorithms set to transform time-consuming molecular screening programs

‘It’s an art and a science,’ explains Joshua Staker, a senior scientist at the US software company Schrodinger. He’s referring to deep learning – a branch of computer science that looks set to transform how chemists screen molecules and explore chemical behaviour.

Over the past few decades, deep learning has entered the public consciousness through projects such as AlphaGo. A landmark in computing, Google’s algorithm is able to autonomously learn and play the board game Go – 1050 times more complex than chess – a challenge once thought to be beyond computers. AlphaGo first defeated a human opponent in 2015, and beat the world number 1 in 2017.

Using algorithms to play games may seem of limited use in science. But if a machine can learn the rules of a game by playing itself, it can learn the rules of chemistry just by analysing chemical data. Deep learning platforms can quickly develop a knowledge of chemistry without any human instruction, and chemists are starting to realise that knowledge can be a powerful tool.

In Schrodinger’s case, Staker and his colleague Kyle Marshall wanted to speed up the process of screening for new drugs and materials by using deep learning to scour the literature for candidate molecules.

Research papers and patents contain huge numbers of molecular structures and experimental data that could be used in virtual screening programs, but getting it out of the documents is laborious. ‘First you have to identify what compounds in the publication you want to actually extract,’ comments Staker. ‘So, you read through the paper and then … go into some drawing software and draw it manually.’ Once the molecule is re-drawn in a computer-readable format (commonly known as SMILES), the information can be used in a screening program.

‘Doing this for hundreds of compounds in a large patent, it becomes tedious,’ laments Staker. ‘[It] starts to become easier and easier to make mistakes over data entry.’

Cutting out the middle man

Staker and Marshall came up with a solution to cut out the middle man. In fact, to cut out men and women altogether. The team has developed a deep neural network that can find images of molecular structures in a document and convert them into a digital format, without being told anything about molecules beforehand. View More

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