Artificially intelligent algorithms can learn to identify amazingly subtle information, enabling them to distinguish between people in photos or to screen medical images as well as a doctor. But in most cases their ability to perform such feats relies on training that involves thousands to trillions of data points. This means artificial intelligence doesn’t work all that well in situations where there is very little data, such as drug development.

Vijay Pande, professor of chemistry at Stanford University, and his students thought that a fairly new kind of deep learning, called one-shot learning, that requires only a small number of data points might be a solution to that low-data problem.

“We’re trying to use machine learning, especially deep learning, for the early stage of drug design,” said Pande. “The issue is, once you have thousands of examples in drug design, you probably already have a successful drug.”

The group admitted the idea of applying one-shot learning to drug design problems was farfetched – the data was likely too limited. However, they’d had success in the past with machine learning methods requiring only hundreds of data points, and they had data available to test the one-shot approach. It seemed worth a try.

Much to their surprise, their results, published April 3 in ACS Central Science, show that one-shot learning methods have potential as a helpful tool for drug development and other areas of chemistry research.

Other researchers have successfully applied one-shot learning to image recognition and genomics, but applying it to problems relevant to drug development is a bit different. Whereas pixels and bases are fairly natural types of data to feed into an algorithm, properties of small molecules aren’t.

To make molecular information more digestible, the researchers first represented each molecule in terms of the connections between atoms (what a mathematician would call a graph). This step highlighted intrinsic properties of the chemical in a form that an algorithm could process.

With these graphical representations, the group trained an algorithm on two different datasets – one with information about the toxicity of different chemicals and another that detailed side effects of approved medicines. From the first dataset, they trained the algorithm on six chemicals and had it make predictions about the toxicity of the other three. Using the second dataset, they trained it to associate drugs with side effects in 21 tasks, testing it on six more.

In both cases, the algorithm was better able to predict toxicity or side effects than would have been possible by chance.

Beyond giving insight into drug design, this tool would be broadly applicable to molecular chemistry. Already, the Pande lab is testing these methods on different chemical compositions for solar cells. They have also made all of the code they used for the experiment open source, available as part of the DeepChem library.

“This paper is the first time that one-shot has been applied to this space and it’s exciting to see the field of machine learning move so quickly,” Pande said. “This is not the end of this journey – it’s the beginning.”
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Source : http://news.stanford.edu/2017/04/03/deep-learning-algorithm-aid-drug-development/

And here we are still battling with ourselves...ooh well,am also working on a project