AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.

Simple cell viability screens fail to identify powerful drug combinations where each component is ineffective on its own. AI can predict these synergies, but only if trained on mechanistic data that reveals how cells rewire their internal pathways in response to a drug.

The degradation mechanism is fundamentally superior to inhibition because it removes the entire protein, addressing both its enzymatic and scaffolding functions. This allows degraders to hit targets harder and more completely, suggesting they could become the dominant modality across oncology and other therapeutic areas.

Pathways like integrins have long been of interest but lacked effective therapeutic approaches. The advent of new technologies, such as antibody-drug conjugates and checkpoint inhibitors, has created opportunities to re-explore these older targets with potent, modern drugs, breathing new life into decades-old research.

Instead of targeting individual gene mutations in diseases like ALS, condensate science focuses on shared cellular structures where genetic risks converge. This approach creates a broader therapeutic target, potentially treating more patients with diverse genetic profiles.

A major challenge in phenotypic drug screening is determining a compound's mechanism of action. AI models can analyze the complex visual data of cellular condensates after drug treatment, extracting maximal information to understand how the drug is actually working inside the cell.

The efficacy of some established drugs, like the chemotherapy oxaliplatin, may be due to an unknown mechanism: they partition into and disrupt cellular condensates. This reframes our understanding of drug action and could explain why certain drugs are more effective in some cancers than others.

A single degrader molecule can destroy thousands of target proteins per hour, a massive improvement over the 1-to-1 stoichiometry of traditional inhibitors. This extreme potency makes them ideal payloads for Degrader-Antibody Conjugates (DACs), combining the precision of antibodies with the power of catalytic degradation.

Targeting the MYC cancer protein presents a dual challenge. Biologically, it's vital for healthy cells, creating a high risk of toxicity. Biophysically, its disordered, 'floppy' structure lacks the defined pockets that traditional drugs need to bind to, making it a 'holy grail' target.