The emerging synergy of experimental and computational approaches for therapeutic modulation of biomolecular condensates

Biomolecular condensates (BCs) are membraneless organelles which play roles in key biological functions such as RNA metabolism, signal transduction and DNA repair, reflecting their importance in cellular organization and function. The dysregulation of condensate self-assembly and its internal material properties due to aberrant phase separation has been linked to neurodegeneration, cancers, viral infections, and cardiac diseases. Consequently, there is growing interest in the discovery and development of therapeutic molecules, referred to as condensate modifiers (c-mods), that specifically target BCs and/or their components which are associated with disease. In this perspective, we first provide readers with a brief overview of the possible modes of action of c-mods and the strategies underlying their design for effective targeting of BCs. Next, we highlight the role of traditional computer-aided drug discovery (CADD) in synergy with modern AI/ML methods in targeting BCs as illustrated in recent studies. Finally, we discuss the physicohemical features of the condensate microenvironment and c-mods that enable the favorable partitioning of the latter, thereby opening new avenues for targeting "undruggable" proteins within the condensate microenvironment. We conclude by providing an overview of the challenges that remain to successfully integrate experiment and computation, and discuss potential strategies to overcome them.