Ab Initio-Quality Electrostatic Potentials for Proteins: An Application of the ADMA Approach
Bibliographic record
Abstract
The electrostatic potential around a molecule is often used to describe reactions, binding, and catalysis mechanisms or to serve as a descriptor in structure−activity relationships and molecular similarity studies. Often, very accurate descriptions of this property are needed that traditionally can be obtained, at least for small molecules, by quantum chemical calculations. The aim of this paper is to extend ab initio-quality quantum chemical accuracy to larger molecules such as proteins. The additive fuzzy density fragmentation (AFDF) principle and the adjustable density matrix assembler (ADMA) method are used to divide large molecules into fuzzy fragments, for which quantum chemical calculations can be done directly using smaller, “custom-made” parent molecules including all the local interactions within a preset distance limit. In the next step, the obtained density matrices of electron density fragments are combined to approximate the global density matrix and the electron density of the whole molecules. These ADMA electron densities are then used to calculate ab inito-quality electrostatic potentials of the large molecules. The accuracy of the method is analyzed in detail by two test cases of a penta- and a hexapetide, and the efficiency of the technique is demonstrated by the calculation of the electrostatic potential of the protein crambin.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".