The discovery of small molecule inhibitors for TOX1 and ERG oncotargets with the development and use of progressive docking PD2.0 approach
Bibliographic record
Abstract
Drug discovery is a rigorous process that can cost up to 3 billion dollars and takes more than 10 years to bring new therapeutics from bench to bedside. While virtual screening (such as molecular docking) can significantly speed up the discovery process and improve hit rates, its speed already lags behind the rate of the explosive growth of publically available chemical databases which already exceed billions of entries. This recent surge of available chemical entities presents great opportunities for discovering novel classes of small molecule drugs but also brings a significant demand for faster docking methods. In the current thesis, we illustrated the need for a faster screening method by virtually screening 7.6 million molecules against Thymocyte selection-associated high mobility group box protein (TOX). Then we demonstrated that the deep learning-based method of ‘Progressive Docking (PD2.0)’ can speed up such virtual screening by up to hundred folds. In particular, by utilizing deep learning QSAR models trained on the docking scores of a subset of the database, one can approximate in an iterative manner the docking outcome of unprocessed entries. We tested the developed method against various targets including ETS transcription factor ERG, Estrogen Receptor Activation Function 2 (ERAF2), Androgen Receptor (AR), Estrogen Receptor (ER), Sodium-Ion Channel (Nav1.7) and many more. In this work, we identified 18 active compounds against TOX with micro-molar potency. We also used the PD2.0 method to dock up to 1.3 billion compounds from the ZINC15 database and demonstrated that this deep-learning-based approach resulted in 65X speed acceleration and 130X Full Predicted Database Enrichment (FPDE) while retaining more than 90% of good hits. We also demonstrate the method’s robustness by docking 570 million compounds from the ZINC15 database into 13 diverse drug targets including ERG.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".