Drug Discovery & Design
Target ID, fragment-based, AI/ML-driven design.
Drug discovery is being reshaped by generative AI, structure prediction, and a renewed appetite for previously undruggable targets. WCPD 2027 will examine clinical-stage molecules designed end-to-end with machine learning — Insilico Medicine's INS018_055 for idiopathic pulmonary fibrosis, Recursion's phenomics-driven pipeline, Isomorphic Labs' AlphaFold 3-enabled programs — alongside the fragment-based and DNA-encoded library workflows that remain industry workhorses. Sessions will cover targeted protein degradation (PROTACs, molecular glues including ARV-471 vepdegestrant), KRAS G12C/G12D inhibitors, and the rapid expansion of macrocycles and oral peptides into traditionally biologic-only target space.
- Generative chemistry and AlphaFold 3-guided structure-based design
- Targeted protein degradation: PROTACs, molecular glues, ARV-471
- KRAS, p53, and previously undruggable target classes
- DNA-encoded libraries and fragment-based lead discovery
- Oral peptides, macrocycles, and beyond-rule-of-five chemistry
- Phenotypic screening and AI-driven phenomics (Recursion, Insitro)
- Covalent drug design and chemoproteomics target ID