Deep Learning–Assisted Discovery of Small-Molecule Modulators Targeting MYC Phase Separation in Solid Tumors - Ross D. Lawrence; Clifford Lawrence; Samuel D. Gutierrez; Miguel J. Peters (International Journal of Clinical and Translational Medicine, 2026)
The oncoprotein MYC is a master transcriptional regulator implicated in the majority of human cancers, yet it has historically been considered undruggable due to its intrinsically disordered regions and lack of a stable binding pocket. Recent discoveries have revealed that MYC undergoes liquid–liquid phase separation (LLPS) to form punctate condensates that selectively modulate its transcriptional activity. This process offers a novel therapeutic vulnerability: small molecules that disrupt MYC phase separation could attenuate oncogenic signaling without requiring direct active-site inhibition. However, rationally designing such modulators is extremely challenging because phase separation involves weak, multivalent interactions across large intrinsically disordered domains. This paper presents a systems-level framework in which deep learning architectures are integrated with biophysical simulations and high-throughput screening data to accelerate the discovery of small-molecule modulators targeting MYC condensates. We analyze the architectural trade-offs between graph neural networks that model molecular interaction surfaces and transformer-based models that capture sequence-to-condensate behavior. The deployment of such models within a federated data infrastructure, combining public and proprietary datasets, raises governance and fairness considerations regarding access to training data and algorithmic bias across patient populations. Robustness under distributional shift is examined through adversarial perturbations of molecular representations. Finally, we discuss policy implications for regulatory approval of condensate-targeting drugs and the sustainability of large-scale deep learning pipelines in pharmaceutical research. By framing the problem as a socio-technical system, this paper illuminates the path toward translating computational insights into clinically viable therapeutics.