The complexity of cell biology and the mechanisms of disease pathogenesis are driven by an intricate regulatory network of genes [Chatterjee and Ahituv, 2017, Theodoris et al., 2015, 2021]. A better resolution of this complex interactome network would enhance our ability to design drugs that target the causal mechanism of the disease rather than interventions that aim to modulate the downstream effects [Ding et al., 2022]. However, accurate inference of gene regulatory networks is challenging. The possible space for genetic interactions is vast [Bunne et al., 2024], the networks to be inferred are highly context-dependent, different cell types and tissue types exhibit different regulatory networks and exhibit significant variations across donors [Chen and Dahl, 2024]. Moreover, the data required to study gene regulatory networks for a specific disease is usually limited and highly specialized, often plagued by experimental artifacts [Hicks et al., 2018]. However, a confluence of recent technological progress promises to make this challenging problem more tractable. The advent of accurate single-cell sequencing technologies that remove the artifacts of bulk cell data, better reflect natural variability, and provide signals at higher resolutions. This, along with the increasing availability of atlas-scale scRNAseq datasets that span an extensive range of diseases, cell types, tissue types, and donors provide an unprecedented opportunity for studying disease mechanisms at scale.

Dr. Tim Guilliams, co-founder and CEO of Healx, is an advocate for harnessing the power of AI to accelerate treatments for rare diseases. Rare diseases are largely overlooked by pharmaceutical companies and traditional drug discovery methods, despite the fact that 1 in 17 people in the UK will be affected by one at some point in their life. Sadly, most rare conditions don't have an approved therapy today, and, in the UK, that means that many of the millions of people dealing with a rare disease will not have access to the treatment--or receive the level of care--that they need. So any step taken by governments to improve outcomes for those with rare diseases is to be welcomed, which is why it was positive to see the Department of Health and Social Care publish its first England Rare Diseases Action Plan this spring, building on the wider UK Rare Diseases Framework published last year. It contains some good proposals on improving diagnosis and access to specialist care--which are both vital--but, arguably, something that will have one of the biggest impacts is unlocking new treatments and making them available to patients. Care is key, but so is an investment in finding cures.

The past decade has seen an important and, for many patients, a life-changing rise in the number of innovative new drugs reaching the market to treat diseases such as multiple sclerosis, malaria, and subtypes of certain cancers (such as melanoma or leukemia). In the United States, the Food and Drug Administration approved an average of 41 new molecular entities (including biologic license applications) each year from 2011 to 2020--almost double the number in the previous decade. Despite the immense costs of such achievements, 2 2. Asher Mullard, "New drugs cost US $2.6 billion to develop," Nature Reviews Drug Discovery, December 1, 2014. A major barrier is the daunting challenge of understanding the multifactorial nature of many diseases coupled with the vast set of variables in therapy design. Very few diseases, such as cystic fibrosis, are linked to variants in single genes. Drug development therefore tends to rely on a reductionist, hypothesis-driven approach that narrows the focus to individual cell types or pathways. Focused assays often based on partial information or informed by animal models that never perfectly reflect human disease then attempt to identify single molecules that will benefit patients.