On How AI Needs to Change to Advance the Science of Drug Discovery

TL;DR

Advancing AI in drug discovery requires integrating causal reasoning to better understand cause-effect relationships amidst complex, large-scale biological data, addressing current limitations of deep learning models.

Contribution

The paper advocates for framing drug discovery as a causal reasoning problem, emphasizing the importance of causality in improving AI models for biomedical research.

Findings

Highlighting the limitations of current deep learning approaches in causality

Proposing causal modeling as a key to better drug discovery

Emphasizing the need for explicit causal reasoning in AI for life sciences

Abstract

Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.