Drug Discovery

Introduction: Standing at the Inflection Point As we conclude the Agentic Omics series in March 2026, we find ourselves at a genuine inflection point. The past two years have witnessed extraordinary progress: AlphaFold 3’s extension to protein complexes and ligands, the emergence of 7B-parameter genome models like Evo, foundation models for single-cell biology achieving clinical utility, and the first wave of agentic systems orchestrating multi-step scientific workflows. Yet we also face sobering realities: Phase III clinical trial results remain the ultimate arbiter of success, regulatory frameworks are still crystallising, and the gap between computational prediction and biological causality remains stubbornly wide. ...

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. ...

Introduction: The Convergence Point In Post 13, we defined agentic AI as systems that autonomously plan, reason, use tools, and execute multi-step scientific workflows. Now we arrive at the central thesis of this entire series: Agentic Omics — the convergence of large language model (LLM) reasoning with domain-specific biological AI models like AlphaFold, ESM, scGPT, and DNABERT to create autonomous systems capable of end-to-end biological discovery. This is not science fiction. As of early 2026, agentic systems are being deployed in operational drug discovery settings at companies like AstraZeneca, with documented implementations compressing workflows that once took months into hours while maintaining scientific traceability (Seal et al., 2025). The question is no longer if this convergence will transform biology, but how — and what architecture will get us there most reliably. ...

The Visible Layer of Biology While genomics reads the book of life and proteomics predicts the machinery that executes it, phenomics observes the actual outcome—the visible traits, cellular morphologies, and clinical presentations that emerge from the interplay of genes, environment, and chance. It is the layer we can see, measure, and often directly connect to disease. Yet phenomics has historically been the poor cousin of molecular omics. High-throughput sequencing transformed genomics and transcriptomics into data-rich disciplines, while phenotyping remained labor-intensive, subjective, and low-throughput. A pathologist examining tissue slides. A physician recording clinical observations. A biologist peering through a microscope. ...

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. ...