Theoretical Derivation of Heuristic Molecular Lipophilicity Potential: A Quantum Chemical Description for Molecular Solvation
Bibliographic record
Abstract
We present the theoretical derivation of a heuristic molecular lipophilicity potential (HMLP), which gives a structure-based and quantum chemical description of an important aspect of molecular solvation. The quantum mechanical electrostatic potential (ESP) V(r) on a formal molecular surface is calculated, and then the molecular lipophilicity potential L(r) is constructed by comparing the local electron density with the ESP on the surrounding atoms using a screening function. The screening function is derived from statistical mechanical theory treating the polar solvent molecules as dipoles. HMLP is able to describe the main interactions of solute molecules with polar and nonpolar solvent molecules. HMLP is a unified lipophilicity and hydrophilicity potential: its positive values represent lipophilicity, and its negative values represent hydrophilicity. In this paper, several examples show that HMLP gives more reliable descriptions for the molecular solvation than some other methods, such as atomic partial charges and the empirical lipophilicity potential.
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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 | 0.001 |
| 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) | 0.004 | 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".