Tutorials¶
Explore GraphFLA with twelve datasets, from protein, enzyme and RNA evolution to chemical reactions, materials, neural architectures and compiler settings. Each tutorial takes you from preparing the data to building a landscape and interpreting its analysis.
Choose an optimization problem below. Every page includes Python code, saved results, a downloadable notebook with its data, and a link to run it in Google Colab.
| Tutorial | Study / dataset | Variables | Objective | Colab |
|---|---|---|---|---|
| Protein engineering | GB1 · Protein G domain B1 | Amino acids at positions 39, 40, 41 and 54 | Maximise IgG-Fc binding fitness | |
| Antibiotic resistance evolution | DHFR · Trimethoprim selection in E. coli | Nine nucleotides in codons 26, 27 and 28 | Maximise fitness under trimethoprim | |
| Ribozyme evolution | Ribozymes · HDV and class III ligase | Nucleotides at 14 variable positions | Maximise log₁₀ ligase activity | |
| Chemical reaction optimization | Suzuki–Miyaura coupling | Ligand, base and solvent | Maximise UV product-area percentage | |
| Reaction process optimization | Electrochemical flow hydrogenation | Concentration, temperature and residence time | Maximise the reported integrated index | |
| Alloy composition optimization | Tungsten–rhenium–osmium alloys | Tungsten and rhenium fractions, with osmium as the remainder | Maximise high-temperature hardness | |
| Materials design | Hybrid perovskites · ABX₃ | Organic ion, metal and halide | Minimise calculated electronic band gap | |
| Microbial growth optimization | BacPUS · Gut bacteria and polysaccharides | Bacterial strain and polysaccharide | Maximise mean growth at 48 hours | |
| Enzyme inhibitor discovery | Cyanimide library · Mouse USP18 | Amine and carboxylic-acid building blocks | Maximise mUSP18 inhibition | |
| Drug combination optimization | NCI-ALMANAC · Cancer cell assays | Partner drug and two dose indices | Minimise percent growth in a fixed cell-line context | |
| Neural architecture search | NAS-Bench-201 · CIFAR-10 | Operations on six architecture edges | Maximise validation accuracy for a fixed training seed | |
| Software configuration tuning | LLVM · Compiler flags | Ten Boolean compiler options | Minimise measured compilation time |