Induced Fit—The Key for Understanding LSD Activity? A 4D-QSAR Study on the 5-HT2A Receptor System
Bibliographic record
Abstract
Using a 4D-QSAR approach (software Quasar) allowing for multiple-conformation, orientation and protonation-state ligand representation as well as for the simulation of induced-fit phenomena, we have validated a family of receptor surrogates for the 5-HT2A receptor system. The evolution was based on a population of 200 receptor models and simulated during 6,000 cross-over steps, corresponding to 30 generations. It yielded a cross-validated r2 of 0.951 for the 23 ligands of the training set and a predictive r2 of 0.859 for the 7 ligands of the test set. In this simulation, all ligand molecules were represented by four different conformers, obtained from a Monte-Carlo search in implicit aqueous solution. A series of six scramble tests (with an average predictive r2 of −1.05) indicate a high sensitivity of the surrogate family towards the biological data. The quantitative analysis of the contribution of the individual functional groups to the free energy of ligand binding, ΔG°, reveals that the key factors for strong binding—and hence activity—are the ligand desolvation energy and the costs associated with induced fit, the adaptation of the receptor-binding site to the ligand topology. While the ammonium functionality is essential for recognition, its contribution to ΔG° is not favorable due to a high desolvation energy; important groups are rather methoxy and halide substituents. For most ligand molecules, the evolution does not select the lowest-energy conformer, contrary to previous assumptions in a corresponding 3D-QSAR study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".