Blog

AI Engineering

From Prompts to Systems: How AI Engineering Changed Between 2023 and 2026

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.

Yassir Boulaamane

• Aug 11, 2026

Claude Code

Getting Real Work Out of Claude Code: A Practical Workflow Guide

A practical guide to working effectively with Claude Code, covering verification loops, CLAUDE.md context, plan modes, tool integration, and permission settings.

Yassir Boulaamane

• Aug 2, 2026

Ai in Drug Discovery

Making AI Work in Discovery Chemistry: Precision, Trust, and Practical Value

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.

Yassir Boulaamane

• Jul 30, 2026

Density Functional Theory

DFT Reproduction of Gold(III)-Mediated Selenocysteine Arylation: An ORCA Tutorial

Computational reproduction of the C-Se reductive elimination step in gold(III)-catalyzed selenocysteine arylation using ORCA, GFN2-xTB, and OpenBabel.

Yassir Boulaamane

• Jul 30, 2026

Artificial Intelligence

Beyond Parameter Counts: The Shift Toward Rigorous Evaluation in Scientific AI

Limitations of scale-based evaluation in structural biology, and the transition toward physical grounding, out-of-distribution generalization, and biophysical developability validation.

Yassir Boulaamane

• Jul 21, 2026

Protein Descriptors

Protein Descriptors for Machine Learning in Drug Discovery

A practical map of protein and protein-ligand descriptors for machine learning modeling, from sequence-only features to MD-derived structural and interaction features.

Yassir Boulaamane

• Jul 20, 2026

Graph Neural Networks

Graph Neural Networks and DGL: A Beginner's Guide

Introduction to graph representation learning, neighborhood aggregation, and Deep Graph Library (DGL) workflows, including a glossary and tutorial reference map.

Yassir Boulaamane

• Jul 10, 2026

Ensemble Docking

Ensemble Docking for Binding and Activity Prediction

Limitations of static single-structure docking for flexible targets, and an integrated workflow combining molecular dynamics, clustering, and machine learning.

Yassir Boulaamane

• Jul 5, 2026

Artificial Intelligence

You Are Not an Impostor: Agentic Coding and the New Computational Scientist

A pipeline I never wrote a line of worked flawlessly on the first try, and it left me with impostor syndrome. Here is why relying on AI agents is not cheating, but a permanent shift in what scientific expertise actually means.

Yassir Boulaamane

• Jun 23, 2026

Machine Learning

Reproducible ≠ Robust: Why One UMAP Seed Isn't Enough to Trust a Split

Pinning random_state=42 makes a UMAP-based train/test split perfectly reproducible - and that is exactly why it can lull you into a false sense of rigor. Reproducibility guarantees you get the same answer every run; it says nothing about whether that answer is typical. Here's the distinction, why it matters for evaluating GNNs on chemical-domain shifts, and how to fix it.

Yassir Boulaamane

• Jun 23, 2026