Extensive Crystallographic Fragment-Based Approach to Design SARS CoV2 3CLpro Main Protease Inhibitors and Related Metadata
Bibliographic record
Abstract
3CLpro is a vital protein for the SARS-CoV-2 replications and its inhibition using small molecules is a bona fide approach used to develop new drugs against the virus. In this study, a comprehensive crystallography-guided fragment-based drug discovery approach was employed to design new inhibitors for SARS-CoV-2 3CLpro. Protein Data Bank was explored to find small molecules cocrystallized with SARS-CoV-2 3CLpro. The fragments sitting in the binding pocket (87) were interactively coupled using various linkers with the intention to get molecules having the same orientation as those of the constituting fragments. In total, 1251 couples were prepared and converted to maximum possible stereoisomers using LigPrep for screening using Glide (standard precision and extra precision), AutoDock Vina, and Prime MMGBSA. Top 22 hits having conformations similar to their cocrystallized fragments were selected for MD simulation on Desmond. MD simulation suggested that 15 hits had conformations very close to their constituting fragments. Results indicated that these hits were computationally reliable and could be considered for further development. This suggests that the study could provide a benchmark starting point for the further design of SARS-CoV-2 3CLpro inhibitors with improved binding (data provided).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".