The Second CACHE Challenge - Targeting the RNA-Binding Pocket of the SARS-CoV2 Nonstructural Protein 13 via a consensus-scoring method and FITTED templated docking.
Bibliographic record
Abstract
Disrupting the Nonstructural Protein 13 (NSP13) in SARS-CoV2 could provide a great avenue for the treatment of COVID-19 and help reduce its enormous health burden. As part of the second CACHE challenge, we targeted each of two sub-pockets of the NSP13 RNA-binding site via a multi-pronged virtual screening (VS) campaign, using the latest functionality in FITTED, our docking program, part of the FORECASTER drug discovery suite. After extensive structure preparation and docking (rigid, flexible), we evaluated predicted poses from the VS using four approaches: docking score, machine learning (graph neural network), quantum-mechanics, and visualization, with the final selection being based on the consensus of all four approaches. Additionally, we implemented templated docking within FITTED to take advantage of fragments co-crystallized with NSP13, which supplemented our consensus selection. We now await the experimental testing of our predictions by the Structural Genomics Consortium, and once available, we will update this manuscript accordingly. In sharing our approach and findings, we hope to continue contributing to open science, and engaging in the ongoing effort of the scientific community towards ending COVID-19.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".