Evaluation of the field-adapted ADMA approach: absolute and relative energies of crambin and derivatives
Bibliographic record
Abstract
A large number of conformations and chemically modified variants of the protein crambin were used to extensively test the field-adapted adjustable density matrix assembler (FA-ADMA) method developed for ab initio quality quantum chemistry computations of proteins and other macromolecules, introduced in an earlier publication. In this method, the fuzzy density matrix fragmentation scheme of the original adjustable density matrix assembler (ADMA) method has been made more efficient by combining it with an approach of using point charges to approximate the effects of additional, distant parts of a given macromolecule in the quantum chemical calculation of each fragment. In this way, smaller parent molecules can be used for fragment generation, while achieving accuracy that can be obtained only with large parent molecules in the original ADMA method. Whereas in both methods the error relative to the Hartree-Fock result can be reduced below any threshold by choosing large enough parent molecules, this can be done more efficiently with the new method. In order to obtain reliable test results for the accuracy obtainable by the new method when compared to conventional Hartree-Fock calculations, we performed a large number of energy calculations for the protein crambin using various conformations available in the Protein Data Bank, various protonation states, and side chain mutations. Additionally, in order to test the performance of the method for protein-solvent interaction studies, the energy changes due to the formation of complexes with ethanol and single and multiple water molecules were investigated.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".