Proteomics

Agents for Drug Discovery: From Target to Molecule The pharmaceutical industry faces a productivity crisis. Developing a new drug costs an average of $2.3 billion and takes 10-15 years, with over 90% of candidates failing in clinical trials. Traditional drug discovery is a sequential, labor-intensive process: identify a target, validate it, screen millions of compounds, optimize leads, test safety, run clinical trials. Each stage can take years. Agentic AI — autonomous systems that reason, plan, and execute multi-step workflows — promises to compress this timeline dramatically. By orchestrating domain-specific models (AlphaFold for structure, ESM for protein embeddings, generative models for molecule design) with LLM reasoning, agents can automate the entire pipeline from target identification to clinical candidate selection. ...

Protein artificial intelligence is, without question, the most mature and publicly celebrated discipline within the “omics” family. When we discuss AI in biology, the conversation inevitably drifts toward the 2024 Nobel Prize in Chemistry—awarded jointly to David Baker for computational protein design, and to Demis Hassabis and John Jumper for protein structure prediction via AlphaFold. However, structure prediction was merely the opening act. Today, the frontier has rapidly shifted from static structure prediction to protein design (creating entirely new proteins), function prediction, and complex interaction modeling. In this seventh installment of the Agentic Omics series, we will dissect the current state of AI in proteomics, evaluate the monumental shifts from AlphaFold 2 to AlphaFold 3 and ESM-3, explore generative models like ProGen and RFdiffusion, and critically assess their real-world clinical impact in drug discovery. ...

In the first post of this series, we mapped the omics landscape: genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics. The next question is obvious: why did AI suddenly get so good at several of these fields at once? The short answer is that biology turned out to be unusually compatible with the same family of models that transformed natural language processing. DNA, RNA, proteins, and even single-cell expression matrices are not “language” in any literal sense, but they are structured symbol systems with long-range dependencies, rich context, and vast quantities of unlabeled data. That is exactly the setting where self-supervised foundation models thrive. ...