Artificial Intelligence in Drug Discovery – What is Realistic, What are Illusions? (Parts 1 and 2; Pinned)

Isidro Cortes and I (Andreas Bender) have recently written a two-piece review on “Artificial Intelligence in Drug Discovery – What is Realistic, What are Illusions?” for Drug Discovery Today, discussing why the impact of AI in the drug discovery area is somewhat lagging behind other areas, such as image and voice recognition. The aim of this article is to illustrate what might be needed to take full advantage of the powerful computational algorithms currently available to us, in particular with respect to the data we need, when aiming to design drugs which are safe and efficacious in humans.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
Andreas Bender and Isidro Cortes-Ciriano

Abstract: Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.

Link to article: https://doi.org/10.1016/j.drudis.2020.12.009

Artificial Intelligence in Drug Discovery – What is Realistic, What are Illusions? Part 2: A discussion
of chemical and biological data

Andreas Bender and Isidro Cortes-Ciriano

Abstract: ‘Artificial Intelligence’ (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.

Link to article: https://doi.org/10.1016/j.drudis.2020.11.037

Any comments welcome, either via email or in the comments below, cheers!

/Andreas

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