AI-Driven Discovery of Modulators of Nucleic-Acid Sensors in Inflammation and Ageing MRC Laboratory of Medical Sciences (LMS) in United Kingdom

Nucleic-acid sensing pathways are vital components of the innate immune system, responsible for detecting misplaced DNA or RNA as signals of infection or cellular damage. Key sensors such as cGAS, RIG-I, MDA5, AIM2, and selected TLRs initiate potent inflammatory responses when activated. While these pathways are essential for host defense, their inappropriate or chronic activation has been directly linked to age-associated inflammation (“inflammageing”), sterile tissue damage, metabolic dysfunction, and multiple age-related diseases. Aberrant cGAS activation by cytosolic or nuclear DNA contributes to cellular senescence, SASP induction, and persistent immune signaling, making the discovery of specific inhibitors or modulators of nucleic-acid sensors a promising strategy to limit maladaptive inflammation and support healthier ageing.

This PhD project at the MRC Laboratory of Medical Sciences aims to develop an artificial-intelligence-driven platform for the discovery of small-molecule and peptide modulators of nucleic-acid sensors, with an initial focus on cGAS. Recent advances in large language models and generative AI have transformed drug discovery, enabling the design and prioritization of compounds that exploit structural and biochemical features learned from vast protein and chemical datasets. The project will leverage these approaches to build models capable of generating and ranking candidate binders targeting specific functional surfaces—such as the DNA-binding interface of cGAS, its nucleosome-interaction region, or allosteric pockets that regulate activation. Similar approaches will later be extended to additional RNA and DNA sensors involved in dysregulated innate immune signaling.

The student will construct and refine AI models to generate novel compounds (small molecules, peptides, mini-proteins) predicted to modulate nucleic-acid sensors. Candidates will be screened and prioritized using AI-based scoring of binding affinity, selectivity, stability, and synthetic feasibility. Structural information from high-resolution cGAS-DNA and cGAS-nucleosome complexes will be integrated to constrain and improve predictions. Lead compounds identified in silico will then be tested experimentally. Biochemical assays will examine their ability to interfere with cGAS–DNA and cGAS–nucleosome interactions. Promising candidates will be evaluated in cell-based models of senescence, where cGAS–STING activation drives inflammatory cytokine production. This will allow determination of whether AI-generated molecules can suppress aberrant innate immune activation and dampen the SASP in relevant physiological contexts.

Overall, this project combines AI-based drug discovery, innate immunity, and cellular ageing biology, contributing directly to the development of targeted anti-inflammatory strategies within the Team Science programme at LMS. The studentship is fully funded for four years, covering all tuition fees and providing a stipend of £26,500 per annum. Overseas students are eligible to apply. Applicants should hold or expect to obtain a first or upper second class degree in a relevant subject such as computer science, biology, chemistry, or related fields, with experience or strong interest in artificial intelligence, machine learning, and drug discovery highly desirable.

To apply, visit the MRC Laboratory of Medical Sciences website and follow the application instructions for LMS 4-year PhD studentships. The deadline for applications is March 10, 2026. For further information, refer to the project page and studentship details provided in the links.