Optimizing Ligand Charges for Maximum Binding Affinity. A Solvated Interaction Energy Approach
Bibliographic record
Abstract
We show that for a given binding site and a given spatial arrangement of atoms in a ligand, there exists an optimal set of partial charges at the atom centers that will optimize the net electrostatic binding free energy of the ligand. This optimal value can be calculated quite readily from a simple quadratic polynomial with coefficients derivable from a few continuum dielectric solvation calculations using a boundary element (BEM) solution of the Poisson equation. Three examples are presented: (a) the binding of cations to 18-crown-6 ether, (b) the calcium-binding sites of parvalbumin, and (c) ligand binding at the active site of the cysteine protease, cathepsin B. The calculations indicate that potassium is the preferred cation for binding to 18-crown-6 ether and that its charge of 1 eu is close to optimal for binding affinity. Similarly, the optimum charge for a monatomic ligand (with a calcium radius) in the calcium binding sites of parvalbumin is predicted to be about 1.8 eu, in agreement with this site's preference for divalent cations. These results show how electrostatics provides a mechanism for binding site specificity for a given ionic valency. For cathepsin B, charge preferences around the active site are probed using both monatomic and multiatomic ligands. The notion of charge complementarity should be extended beyond the pairing of oppositely charged groups to also include the selection of the correct charge magnitudes. The concept of optimum ligand charges has profound implications for understanding molecular recognition and for molecular design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".