Docking Ligands into Flexible and Solvated Macromolecules. 3. Impact of Input Ligand Conformation, Protein Flexibility, and Water Molecules on the Accuracy of Docking Programs
Bibliographic record
Abstract
Several modifications and additions to Fitted1.5 led to the development of Fitted2.6. Among the novel implementations are a matching algorithm-enhanced genetic algorithm and a ring conformational search algorithm. With these various optimizations, we also hoped to remove the biases and to develop a docking program that would provide results (i.e., poses) as independent as possible to the input ligand and protein conformations and used parameters, although keeping the options to provide additional experimental information. These biases were investigated within Fitted2.6 along with FlexX, GOLD, Glide, and Surflex. The input ligand conformation was found to have a major impact on the program accuracy as drops as large as 10-50% were observed with all the programs but Fitted. This comparative study also demonstrates that the accuracy of Fitted is similar to that of other widely used programs. We have also demonstrated that protein flexibility, displaceable water molecules, and ring conformational search algorithms, three of the main Fitted features, significantly increased its accuracy. Finally, we also proposed potential modifications to the available programs to further improve their accuracy in binding mode prediction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".