Drug/discovery/structural biology

• Done. I ran the new ALDH + NAD + metabolite panel and the GSTM2 + GSH + 5-S-cysteinyl-DOPAC deep-damage extension,
then folded everything into one combined note at C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-
3398%20(1).vcf/gene_sequences/aldehyde_and_conjugation_branch_panel.md.

For the aldehyde side, all six jobs completed: ALDH1A1/ALDH2 + NAD + DOPAL/DOPAC/DOPEGAL. The main output is C:/Users/
User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/gene_sequences/boltz2_aldh/results/
boltz2_aldh_branch_note.md with metrics in C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-
3398%20(1).vcf/gene_sequences/boltz2_aldh/results/boltz2_aldh_branch_metrics.tsv. DOPAL ranked strongest in both
ALDH1A1 and ALDH2, DOPEGAL was next, and DOPAC was weakest. The important caveat is that all ALDH states had very
large complex_ipde values, so this is useful as branch triage, not a clean docking-style answer.

For the conjugation side, I reused the existing GSTM2 jobs, added GSTM2 + GSH + 5-S-cysteinyl-DOPAC, and reran the
structure-level analysis. The updated note is C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-
3398%20(1).vcf/gene_sequences/boltz2_gstm2/results/boltz2_gstm2_gsh_structure_note.md, with tables in C:/Users/
User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/gene_sequences/boltz2_gstm2/results/
boltz2_gstm2_gsh_structure_metrics.tsv and C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-
3398%20(1).vcf/gene_sequences/boltz2_gstm2/results/boltz2_gstm2_gsh_structure_samples.tsv. The ranking is now:

  1. GSTM2 + GSH + 5-S-cysteinyl-dopamine
  2. GSTM2 + GSH + aminochrome
  3. GSTM2 + GSH + 5-S-cysteinyl-DOPAC
  4. GSTM2 + GSH + dopamine o-quinone

New scripts are C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/
run_boltz_aldh_branch_jobs.py, C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/
analyze_boltz_aldh_branch.py, and C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/
run_boltz_gstm2_gsh_deeper_damage_jobs.py. I did not run AKR1C yet; that stayed out of scope for this pass.

› so how well does GSTM2 rescue these dopamine metabolites

• GSTM2 looks plausible for the conjugation and downstream-damage branch, but I would not call it a proven “rescue” yet.

What the current Boltz panel says:

  • Best GSTM2-side state is GSTM2 + GSH + 5-S-cysteinyl-dopamine: binding_confidence 0.616673, complex_ipde 0.731850,
    with coherent GSH geometry and a moderately recurring probe family. That is the strongest structural support for GSTM2
    participating after quinone trapping, not before it.
  • GSTM2 + GSH + aminochrome is decent by score (0.603468) but structurally looser. The probe RMSD is large (9.327 Å),
    so it looks less like a clean recurring handled state.
  • GSTM2 + GSH + 5-S-cysteinyl-DOPAC is actually quite clean geometrically: binding_confidence 0.498895, complex_ipde
    0.601556, probe RMSD 0.614 Å. It looks like a believable deeper-damage/conjugate branch state, but weaker overall than
    5-S-cysteinyl-dopamine.
  • GSTM2 + GSH + dopamine o-quinone is the weakest practical “rescue” read. The probe is very consistent, but it sits
    farther from GSH, which makes it look more like association than active conjugation coupling.

So the honest read is:

  • GSTM2 looks good for handling downstream conjugated dopamine-oxidation damage.
  • It looks less convincing as the primary answer for upstream free dopamine quinones.
  • If you want “rescue of dopamine metabolites,” the strongest GSTM2 case is 5-S-cysteinyl-dopamine, then 5-S-
    cysteinyl-DOPAC, then aminochrome, then raw dopamine o-quinone.

The relevant outputs are C:/Users/User.codex/alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/gene_sequences/
boltz2_gstm2/results/boltz2_gstm2_gsh_structure_note.md and the combined summary C:/Users/User.codex/
alex_chen_nucleus_dna_download_vcf_NU-PGIK-3398%20(1).vcf/gene_sequences/aldehyde_and_conjugation_branch_panel.md.

If you want the shortest conclusion: GSTM2 probably helps mop up the conjugated damage branch, but NQO1 and ALDH still
look more relevant for the earlier upstream toxic intermediates.


Ash Jogalekar


@curiouswavefn

·

7h

While impressive in its own right, it’s worth remembering that predicting protein-drug interactions is like 0.01% of the drug discovery and development pipeline and the spare change part of it. Saying that that’s revolutionizing the process is like convincing someone, in Derek

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Ian Miles Cheong

@ianmiles

·

Mar 31

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact.


Hang Zheng


@HangZheng855161

Exactly. Structure prediction is solved enough for most practical purposes, but the bottleneck was never the structure — it’s the biology downstream. PK, toxicity, formulation, clinical translation. AI is making real contributions in pockets (FEP, generative chemistry), but the 0.01% framing is a healthy corrective.

4:20 AM · Apr 1, 2026

·


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