What successful incretin drugs, BPC-157, and Epitalon teach us about peptide engineering, translational evidence, and the limits of molecular plausibility.
PCA, t-SNE, and UMAP can produce very different views of the same molecular dataset. The reason is simple: chemical space depends on how molecules are represented, how similarity is defined, and how that high-dimensional information is projected.
What EFMC-ISMC 2026 revealed about the changing practice of drug discovery: from generative chemistry and automated DMTA cycles to allosteric pockets, DMPK liabilities, and human decision-making.
How metastable states turn fragmented molecular dynamics trajectories into kinetic models—and how to distinguish meaningful molecular states from attractive but misleading clusters.
A practical map of how enhanced-sampling and rare-event methods reveal molecular pathways, free-energy landscapes, and kinetics—and what each method can legitimately tell us.
The shift from prompt-centric AI usage to full-stack systems engineering, covering context engineering, agentic loops, retrieval pipelines, evaluation frameworks, and cost-aware deployment.
A practical guide to working effectively with Claude Code, covering verification loops, CLAUDE.md context, plan modes, tool integration, and permission settings.
Key takeaways from the Optibrium panel discussion featuring Nathan Brown, Charlotte Deane, Pat Walters, Chris Swain, and Paul Czodrowski on AI strategy, data leakage, generative risks, and LLMs in drug discovery.
Computational reproduction of the C-Se reductive elimination step in gold(III)-catalyzed selenocysteine arylation using ORCA, GFN2-xTB, and OpenBabel.
Limitations of scale-based evaluation in structural biology, and the transition toward physical grounding, out-of-distribution generalization, and biophysical developability validation.
