NRGRank: Coarse-grained structurally-informed ultra-massive virtual screening
Bibliographic record
Abstract
Abstract NRGRank is a coarse-grained structurally-informed virtual screening Python package with accuracy comparable to docking-based methodologies but up to 100-fold speed increase. NRGRank is based on a coarse-grained evaluation of pairwise atom-type pseudo-energy interactions that implicitly accounts for compound and side-chain flexibility as well as limited backbone movements. We compare NRGRank to docking-based virtual screening software Glide, Autodock Vina and DOCK 3.7 on the DUD-E virtual screening benchmark using enrichment factors at 1% (EF1). We observe broad variations of EF1 values across targets, structural models and methods. For apo form or AlphaFold2 models, out of a subset of 37 targets from DUD-E, NRGRank has better EF1 values than Glide for 12 and 13 targets respectively. Even in holo form, where the accuracy of classical docking software increases, NRGRank has better EF1 values than 13, 10 and 5 targets out of 37 compared to AutoDock Vina, DOCK 3.7 and Glide respectively. Comparing the rank of true binders in Glide and NRGRank shows that true binders ranked in the top 1% are complementary between methods irrespective of the target form (AlphaFold, apo or holo). That is, utilizing NRGRank detects binders that are missed by Glide (and presumably other methods), whereas those found by Glide are missed by NRGRank. Furthermore, we observe that most hits found by NRGRank within the top 50 predictions (4.38 ± 5.49 hits on average for AF2 targets) remain once the top 1% of predictions are re-scored with Glide, but the hit rate within the top 50 predictions increases. NRGRank can evaluate one molecule in 0.3 s on average, enabling a modern laptop with 8 cores to screen 1,000,000 molecules in 24 hours – up to two orders of magnitude faster than the reported speed of DOCK 3.7, AutoDock Vina running on GPUs and Glide. NRGRank occupies a unique niche among tools for virtual screening being insensitive to structural inaccuracies but with comparable accuracy as state-of-the-art docking methods and fast as AI-based methods but without the dangers of overfitting as it is based on 780 pseudo-energy parameters. Combined with the fact that NRGRank does not require extensive or expensive computational resources or expert pre-processing of targets, it is unique in making high-performance ultra-massive virtual screening accessible to all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".