Tailored Parameterization of the LIE Method for Calculating the Binding Free Energy of Vps34–Inhibitor Complexes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Vps34 is the only isoform of the PI3K family in fungi, making this protein an attractive target to develop new treatments against pathogenic fungi. The high structural similarity between the active sites of the human and fungal Vps34 makes repurposing of human Vps34 inhibitors an appealing strategy. Nonetheless, while some of the cross-reactive inhibitors might have the potential to treat fungal infections, a safer approach to prevent undesired side effects would be to identify molecules that specifically inhibit the fungal Vps34. This study presents the parameterization of four LIE models for estimating the binding free energy of Vps34–inhibitor complexes. Two models are parameterized using a multiparametric linear regression leaving one or more free parameters, while the other two are based on the LIE-D model. All of the models show good predictive capacity ( R 2 > 0.7, r > 0.85) and a low mean absolute error (MAE < 0.71 kcal/mol). The current study highlights the advantages of LIE-D-derived models when predicting the weight of the different contributions to the binding free energy. It is expected that this study will provide researchers with a valuable tool to identify new Vps34 inhibitors for relevant applications such as cancer treatment and the development of new antimicrobial agents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".