[Interview] Adoption of AI-driven Tools By The Life Sciences Professionals: What Is Coming In 2018?

The previous year was rich in discussions and events one way or another related to potential applications of artificial intelligence (AI) advances for the benefit of drug discovery and development.

(Note: For the sake of simplicity, the term “AI” will be applied herein interchangeably with terms like “machine learning” (ML), “deep learning” (DL), “neural networks” (NN) etc., although conceptually, those terms are quite different in meaning. The term AI describes a field of computer science studying how to make a computer intelligent at doing something, while terms like ”machine learning”, “deep learning”, and “neural networks” relate to algorithms and methods by which it can be achieved.).

To briefly refresh in memory what was going on in 2017, here is a brief report providing a condensed overview of the field, and here is a list of the most active AI-driven startups developing tools to assist pharmaceutical and biotech research. The most actively explored use cases for the AI in drug discovery are categorized in the article Biopharma’s Hunt For Artificial Intelligence: Who Does What?.

According to a survey conducted by The Pistoia Alliance -- a not-for-profit organization engaged in the advancement of new technologies and data standardization practices in the life sciences industry -- almost half of the respondents (44%) among 374 surveyed life science professionals from US, Europe, Russia and China, are already using or interested in using AI in their research.

Among the most pressing challenges for the AI adoption, the respondents pointed out a lack of technical expertise in AI, ML and NLP, limited data access, quality, and lack of standardization were also regarded as considerable obstacles.

To find out more insights about how the AI adoption by life science professionals will be happening in 2018, I reached out to Ed Addison, Co-founder and CEO at Cloud Pharmaceuticals -- one of the leading players in the emerging “AI-for-drug-discovery” space: