Expanding the targets of molecular glue cancer drugs: E3 ligase and neosubstrate discovery strategies
Background
To broaden the cancer targets of molecular glue degraders, development strategies focused on cereblon (CRBN) need to extend to other E3 ubiquitin ligase pathways. A review in Oncology Research surveys the discovery tools, established examples, and remaining development challenges involved in this expansion. It synthesizes findings from multiple studies rather than reporting experimental results from a new integrated platform.
Molecular glue degraders (MGDs) are small molecules that induce or stabilize interactions between a target protein and an E3 ligase. Ubiquitination of the target within the resulting complex can lead to proteasomal degradation. A target that a ligase does not normally recognize but newly recognizes because of a drug is called a neosubstrate. Unlike conventional inhibitors that block protein function, this approach promotes removal of the protein itself.
The human genome encodes more than 600 E3 ligases, but degrader development has focused on only a subset. In particular, CRBN-centered approaches constrain the range of degradable targets and tissue selectivity. Drug responses may also weaken if expression of the recruited ligase decreases in cancer cells or mutations arise at its binding interface with the target.
Key findings
The review compares known molecular glue–ligase–target combinations and describes pathways beyond CRBN. CRBN is the substrate-recognition protein of an E3 ligase complex. A prominent example is the mechanism by which thalidomide derivatives recruit CRBN to induce degradation of the multiple myeloma-associated transcription factors IKZF1 and IKZF3. By contrast, arylsulfonamides such as indisulam and E7820 recruit DCAF15, a different substrate-recognition protein, to induce degradation of RNA-binding protein 39 (RBM39). Acute myeloid leukemia is one disease in which this approach is being studied.
Tools for finding these combinations answer different questions. Chemoproteomics identifies proteins bound by small molecules, while quantitative proteomics examines which proteins decrease after drug treatment. Ubiquitin-remnant analysis identifies the sites on proteins where ubiquitin has been attached. Combining these methods with functional genomics based on the gene-editing tool CRISPR can test whether a particular ligase or cofactor is required for degradation.
Structural analysis and computational modeling help clarify how candidate combinations work. This involves identifying degrons, the signals for target recognition and degradation, and analyzing the binding interfaces in the ternary complex formed by the ligase, small molecule, and target. Artificial intelligence (AI)-based predictions can help prioritize candidates, but separate experiments are needed to determine whether predicted binding leads to actual ubiquitination and degradation. The review did not directly map more than 600 ligases or measure catalytic constants for new combinations.
Implications and outlook
A wider choice of ligases could create opportunities to explore targets that are difficult to reach through existing pathways and to consider strategies that use differences in tissue expression to improve selectivity. However, not every ligase discussed as a candidate has been validated as a molecular glue platform. DTX1, which the review also discusses, is an exploratory candidate proposed on the basis of its biological function; this does not mean that a molecular glue recruiting it has been established.
A central development challenge is confirming that only the intended protein is degraded in the desired cells. Degradation of unintended neosubstrates, differences in ligase activity across tissues, and the emergence of resistance remain unresolved issues. Computational models also face limitations from incomplete training data and limited structural information, and cannot fully capture dynamic interactions inside cells. The direction outlined by the review is to refine candidates through repeated cycles of experiments and prediction. It should not be interpreted as demonstrating the immediate design of patient-specific degraders or the clinical efficacy of new candidates.
The mechanisms and discovery strategies are described in the full review in Oncology Research and its bibliographic record and abstract.
Cancer Discovery, Published June 2026.
Summary: Bypassing the operational constraints and resistance profiles driven by the heavy reliance on a limited set of E3 ligases—specifically Cereblon (CRBN)—within the current molecular glue degrader (MGD) modality, this study establishes a multidisciplinary discovery pipeline. By coupling chemoproteomics and quantitative ubiquitin-remnant profiling with artificial intelligence-driven structural modeling, the computing platform systematically interrogates non-canonical E3 ubiquitin ligases and their corresponding oncogenic neosubstrates. This biophysical optimization delineates the mechanistic basis of structural ligase-substrate recognition and degron mapping kinetics, delivering a generalizable computational baseline for the rational engineering of next-generation degraders with expanded substrate diversity and elevated tissue selectivity in translational oncology.
Drug discovery teams can use this review to design experiments that test how candidate degraders work. For example, if a target protein decreases after drug treatment, quantitative proteomics can assess changes in other proteins, while ubiquitin-remnant analysis can identify the sites of modification. Researchers can then remove a specific E3 ligase and test whether degradation stops to assess that pathway’s involvement. This is an application scenario that connects methods described in the review. Checking unintended neosubstrates early can also help assess candidate selectivity and safety. These analyses alone cannot establish therapeutic effects or toxicity in patients; structural, biochemical, and cellular validation, followed by clinical evaluation, would still be required.