In the past decade, membraneless assemblies known as biomolecular condensates have been reported to play key roles in many cellular functions by compartmentalizing specific proteins and nucleic acids in subcellular environments with distinct properties. Furthermore, growing evidence supports the view that biomolecular condensates often form by phase separation, in which a single-phase system demixes into a two-phase system consisting of a condensed phase and a dilute phase of particular biomolecules. Emerging understanding of condensate function in normal and aberrant cellular states, and of the mechanisms of condensate formation, is providing new insights into human disease and revealing novel therapeutic opportunities. In this Perspective, we propose that such insights could enable a previously unexplored drug discovery approach based on identifying condensate-modifying therapeutics (c-mods), and we discuss the strategies, techniques and challenges involved.

Although it is extremely challenging to invent new medicines, I have observed that certain behaviors seem to be commonly found among successful medicinal chemists. Those who exhibit most of these character traits are far more likely to bring new drugs into the clinic and onto the market. And, importantly, organizations that encourage these behaviors are far more likely to be successful. These traits can be broken into two categories: “general” and “discipline-specific.” General traits are those that are common to all great scientists, while the discipline-specific ones are more specialized behaviors relevant to the medicinal chemistry enterprise. I describe these traits, and include some specific examples for each of the medicinal chemistry characteristics that I hope will be illustrative. While success in drug discovery is never guaranteed, I believe that embracing and encouraging these behaviors increases the probability of a successful outcome.

Drug discovery often fails due to unpredictable ADMET issues, which account for 30% of clinical setbacks. Conventional methods lack the atomistic detail needed to navigate the "Avoid-ome"-a finite set of proteins acting as "anti-targets". OpenADMET is an open-science initiative addressing this by creating pre-competitive, mechanistic datasets. Using high-throughput structural biology, active learning, and community challenges, it builds generalizable models grounded in structural "ground truth". By directly studying the Avoid-ome, OpenADMET facilitates an era of rational, multi-parameter drug design.