The AI Productivity Game in Pharma

The pharmaceutical business is one of the riskiest industries to venture into. Drug discovery is an artisanal process where a carefully designed drug takes about 10 years and approximately 2.5 billion dollars to be approved and launched into the market. The complexity of biological systems places the odds at a ridiculous failure rate of 90%. In recent years, the declining efficiency of the R&D efforts has put the pharma industry on its toes.

In the past decade, Artificial Intelligence (AI) has already revolutionized several industries, including automotive, entertainment and fintech. AI dictates routes and ETA on google maps, executes multiple stock exchange transactions, enables facial recognition, and powers the voice assistants Siri and Alexa. However, the adoption of AI in pharma has been restricted due to limited data available about what works (the successful 10%) and the innate complexity of the process of drug discovery.

The sudden hype of AI in pharma

The pharma industry has been using elements of machine learning (a type of AI algorithm) in drug discovery R&D for at least 2 decades now. The most common software used by medicinal chemists, Schrodinger’s suite, has been offering regression analysis for quite some time now. So, what’s new?

However, the recent widespread interest has been fueled by the breakthrough made in neural networks, beginning with ‘AlexNet’ in 2012. AlexNet, a convolution neural network, augmented with supervised learning, could classify images with outstanding precision. It was not too late that these algorithms found its way in chemistry to classify drug molecules. Subsequent publishing of Generative Adversarial Networks (GANs) or often referred to as ‘creative AI’ in 2014, combined with reinforcement learning that could be used to generate novel molecule entities with a desired set of pharmacological properties.

By 2018, Natural Language Processing (NLP) and computer vision algorithms, which can generate insights by crawling through millions of papers, patents, grant, clinical trials data etc., made significant progress and allowed making sense of vast amount of fragmented data. Simultaneously, advances in -omics and other high through put techniques generating the big data allowed pharma to use to AI efficiently.

These advanced methods could be efficiently trained to predict/generate novel chemical structures with desired pharmacological properties, learn systems biology to identify new targets/biomarkers, predicting toxicity, and many other applications in drug discovery and development.