Hit to lead

Lock the positions of a scaffold that must survive, grow the rest with DrugEx, then narrow the result stage by stage. Every compound keeps the identifier it was given at generation, so each number on it can be traced back.

Pick atoms to keep

kept — becomes iodine no hydrogens — cannot be locked

Selection

0
atoms
0
iodines
0
lockable
Nothing selected yet

Pipeline

Generate not run

Khanh's own round is 100 molecules at 0.2 (lead_generation_until_N.sh). SA is normalised 0–1, not the raw 1–10 Ertl scale, and higher means easier to make. Measured on this scaffold: 0.2 kept 96 of 100, 0.4 kept 28 of 40, and 0.6 and 0.8 kept nothing at all.

PAINS and BRENK not run

Rejects on structure alone, so it has nothing to tune. Compounds matching a known interference or unwanted-chemistry pattern are removed.

Properties at pH 7.4 not run

Both pH stages return identical results today. compound_properties_filter.py calls obabel -p <pH> -h, and the -h cancels the pH model, so every property is computed on the neutral molecule. Question 7 for Khanh.

Properties at pH 6.3 not run

Same ranges as pH 7.4, applied again at the pH of the Golgi, where FUT8 sits. 7.4 is the cytosol.

pKa not run
Docking not run

Upload the original PDB, not a prepared PDBQT. A prepared file has already had its ligands stripped, and a bound ligand is the most reliable way to place the box.

Khanh's scripts disagree: run_docking_pipeline.sh uses −5.0, run_filter_docking.sh uses −9.0. More negative is better, so −9.0 is far stricter. Pick deliberately.

Scores are not reproducible. vinascreen.py passes no seed to Vina and offers no way to, so the same compound scores differently on every run — measured spread up to 0.26 kcal/mol against the shipped example. A compound whose score sits near the cutoff survives or dies by the run. Question 8 for Khanh.

Predicted affinity not run

The sequence in the repository belongs to a third protein, neither FUT8 nor the shipped receptor, so there is no safe default.

One GPU prediction per compound. Measured on dichtator: about 115 s each including the shared MSA, so 25 compounds is roughly 45 minutes and 10,000 would be a fortnight.

This machine has about 7.5 GB of VRAM and a protein of 369 residues needs about 17 GB, so affinity runs elsewhere. A venv binary is not on a non-interactive ssh PATH, which is why the command is a full path. Khanh’s script hardcodes GPU 1; on dichtator that is the busy card.