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.

Professor Collins' AI models, trained only to kill a specific pathogen, unexpectedly identified compounds that were narrow-spectrum—sparing beneficial gut bacteria. This suggests the AI is implicitly learning structural features correlated with pathogen-specificity, a highly desirable but difficult-to-design property.

Just as biology deciphers the complex systems created by evolution, mechanistic interpretability seeks to understand the "how" inside neural networks. Instead of treating models as black boxes, it examines their internal parameters and activations to reverse-engineer how they work, moving beyond just measuring their external behavior.

While AI is a universal trend, its application is highly contextual. In drug discovery, it's used for complex, high-science tasks like protein folding. In the CDMO space, its value lies in streamlining less glamorous but critical functions like communication, paperwork, and process optimization.

An AI model analyzing drug delivery peptides discovered that adding a flexible amino acid before the active end group significantly improved cell entry. This was not a commonplace understanding in the field. Initially questioned by chemists, the insight was experimentally validated, showing how AI can augment human expertise by revealing novel scientific mechanisms.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

To target MYC, Dewpoint uses phenotypic screens that monitor the entire MYC condensate. This approach is mechanism-agnostic, capable of identifying compounds that work via previously attempted methods (e.g., disrupting binding) as well as novel ones like dissolving the condensate itself.