hitdisc-20261022-use-case-summary: WF1696 wording corrected to the source's own

Stable ID: metadata-correction-af9e1e5f966877013dbf

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Three sources in this store score machine-learning and co-folding methods against docking on the same screening data, and what the numbers say depends on how the negatives were built. On DEKOIS 2.0, whose 79 targets each pair 40 validated actives with 1,200 property-matched decoys selected from ZINC (1:30), the co-folding confidence scores enrich strongly and beat the docking arms. AF3 min-iPAE gives a mean EF1% of 23.15 and Boltz-2 min-iPAE 22.73, against 12.01 for the Glide SP row quoted from another paper, 5.16 for Gnina Vina and 2.94 for TankBind; the best rescoring of a co-folded pose, RTMScore on AF3 poses, reaches 21.34. The authors flag their own Boltz-2 affinity arms (mean EF1% 23.54 and 25.84) as likely over-estimates because Boltz-2 drew affinity data from the same public databases that source DEKOIS 2.0. On GPCRrecent, 16 targets whose first crystal structures were released after 2022 with decoys generated by a conditional recurrent neural network at 1:50, every value falls and the margin narrows: Boltz-2 min-iPAE gives a mean EF1% of 13.96 against 5.02 for Glide SP and 3.65 for Gnina Vina. On the five LIT-PCBA targets where the inactives were experimentally measured, the margin largely disappears: averaged over the five targets Boltz-2 min-iPAE gives EF1% 9.30 and NEF1% 0.100 against Glide SP's 6.79 and 0.079, and on TP53 both Boltz-2 scores give EF1% 0.00 while Protenix ipTM gives 5.00 and Glide SP 3.75. A separate audit of LIT-PCBA reports 323 active training-validation analogue pairs at ECFP4 Tanimoto at or above 0.6 in ALDH1 alone and 2,491 2D-identical inactives shared between training and validation across targets, and concludes that a parameter-free memorisation baseline matches or exceeds published deep-learning results there, so every LIT-PCBA value above carries that limit. Where the negatives are compounds a docking screen already ranked highly and every compound was synthesised and assayed, the ordering changes. On the AmpC list (247 inhibitors among 1,293 tested compounds from a 1.7-billion molecule screen) DOCK3.7 separates binders best, AUC 76.5, against 68.1 for Boltz-2 pIC50 and 60.5 for AF3 L-pLDDT. On the dopamine D4 list (205 binders among 541) docking and Boltz-2 are indistinguishable at the precision printed (both about 71) and AF3 L-pLDDT is worse than random at 46.4. Only on the sigma-2 list (201 binders among 506) does a co-folding score lead, Boltz-2 pIC50 at 83.8 against DOCK3.7's 78.8, with AF3 L-pLDDT at 56.1. In the one blinded prospective challenge, CACHE #3 on the SARS-CoV-2 Nsp3 macrodomain, 23 workflows selected 1,739 compounds that were synthesised and assayed; 28 advanced to hit expansion and 29 confirmed hits scored above 15, four of them judged chemically novel. Twelve of the 23 workflows returned no confirmed hit from their own selection of 41 to 99 compounds. When every workflow was applied to the same merged 1,739-compound library, WF1696, which the article describes as a purely physics-based method, captured 2 of the 4 Tier 1 and 9 of the 25 Tier 2 confirmed hits from 99 predicted actives, which the authors report as significantly more hits than any other workflow. Cost bounds all of this: co-folding one protein-ligand pair takes 7.79 to 34.58 seconds per compound on one NVIDIA H100 with pre-computed MSAs, which is why the LIT-PCBA arm covers five targets rather than fifteen. No docking tool was timed on the same hardware, no ligand-similarity or pharmacophore baseline is scored anywhere in this batch, and no result here estimates the hit rate of an untested library.

value

Three sources in this store score machine-learning and co-folding methods against docking on the same screening data, and what the numbers say depends on how the negatives were built. On DEKOIS 2.0, whose 79 targets each pair 40 validated actives with 1,200 property-matched decoys selected from ZINC (1:30), the co-folding confidence scores enrich strongly and beat the docking arms. AF3 min-iPAE gives a mean EF1% of 23.15 and Boltz-2 min-iPAE 22.73, against 12.01 for the Glide SP row quoted from another paper, 5.16 for Gnina Vina and 2.94 for TankBind; the best rescoring of a co-folded pose, RTMScore on AF3 poses, reaches 21.34. The authors flag their own Boltz-2 affinity arms (mean EF1% 23.54 and 25.84) as likely over-estimates because Boltz-2 drew affinity data from the same public databases that source DEKOIS 2.0. On GPCRrecent, 16 targets whose first crystal structures were released after 2022 with decoys generated by a conditional recurrent neural network at 1:50, every value falls and the margin narrows: Boltz-2 min-iPAE gives a mean EF1% of 13.96 against 5.02 for Glide SP and 3.65 for Gnina Vina. On the five LIT-PCBA targets where the inactives were experimentally measured, the margin largely disappears: averaged over the five targets Boltz-2 min-iPAE gives EF1% 9.30 and NEF1% 0.100 against Glide SP's 6.79 and 0.079, and on TP53 both Boltz-2 scores give EF1% 0.00 while Protenix ipTM gives 5.00 and Glide SP 3.75. A separate audit of LIT-PCBA reports 323 active training-validation analogue pairs at ECFP4 Tanimoto at or above 0.6 in ALDH1 alone and 2,491 2D-identical inactives shared between training and validation across targets, and concludes that a parameter-free memorisation baseline matches or exceeds published deep-learning results there, so every LIT-PCBA value above carries that limit. Where the negatives are compounds a docking screen already ranked highly and every compound was synthesised and assayed, the ordering changes. On the AmpC list (247 inhibitors among 1,293 tested compounds from a 1.7-billion molecule screen) DOCK3.7 separates binders best, AUC 76.5, against 68.1 for Boltz-2 pIC50 and 60.5 for AF3 L-pLDDT. On the dopamine D4 list (205 binders among 541) docking and Boltz-2 are indistinguishable at the precision printed (both about 71) and AF3 L-pLDDT is worse than random at 46.4. Only on the sigma-2 list (201 binders among 506) does a co-folding score lead, Boltz-2 pIC50 at 83.8 against DOCK3.7's 78.8, with AF3 L-pLDDT at 56.1. In the one blinded prospective challenge, CACHE #3 on the SARS-CoV-2 Nsp3 macrodomain, 23 workflows selected 1,739 compounds that were synthesised and assayed; 28 advanced to hit expansion and 29 confirmed hits scored above 15, four of them judged chemically novel. Twelve of the 23 workflows returned no confirmed hit from their own selection of 41 to 99 compounds. When every workflow was applied to the same merged 1,739-compound library, WF1696, which the article describes as a purely physics-based method, captured 2 of the 4 Tier 1 and 9 of the 25 Tier 2 confirmed hits from 99 predicted actives, which the authors report as did better than any other method in retrieving hits from the collection of all molecules selected in Round 1. Cost bounds all of this: co-folding one protein-ligand pair takes 7.79 to 34.58 seconds per compound on one NVIDIA H100 with pre-computed MSAs, which is why the LIT-PCBA arm covers five targets rather than fifteen. No docking tool was timed on the same hardware, no ligand-similarity or pharmacophore baseline is scored anywhere in this batch, and no result here estimates the hit rate of an untested library.

source locator

Herasymenko et al. 2026, Results 'Trends and Strategies from the Best Performing Computational Workflows'

review

method: scope-review; independent-cell-check; reviewer: claude; reviewer note: Claude review agent, independent of the extraction agent; no human review claimed; date: 2026-10-22; method note: Read both sentences in the pinned article: 'significantly more hits' belongs to WF1715 and Figure 6b, not to WF1696.; note: Correction found by the independent review pass of batch use-case-coverage-hitdisc-20261022. The previous value is kept here.; receipt: data/omics/use-case-coverage-hitdisc-20261022/review.json

• subject: Summary of the evidence for use-case-hit-discovery-virtual-screening