Methods for Docking Small Molecules to Macromolecules: A User’s Perspective. 1. The Theory
Bibliographic record
Abstract
Over the last two decades, computationally docking potential protein ligands (e.g., enzyme inhibitors) has become one of the most widely used strategies in computer aided drug design. While these docking methods were developed, some effort focused on their user-friendliness up to a point where they can be used by non-experts with nearly no training, somewhat hiding the underlying theory. However, basic knowledge is still required to avoid pitfalls and misinterpretations of docking experiments. Over the years, we have collected the common mistakes and necessary information for the proper use of docking programs. In this review, we compiled this data for non-experts in the field. In a first section, we present the theory of docking and scoring approaches as well as their limitations, followed by the most recent progress towards the consideration of protein flexibility, water molecules, metal ions, and covalent drugs. In a second section, we describe what we believe are the necessary steps to ensure optimal docking. More specifically, we present the selection of a docking program, available databases of small molecules, macromolecules and biological data, the necessary steps for the preparation of proteins and small molecules, and finally post docking analysis techniques. In the following sections, we compile the sources of biases and describe docking to nucleic acids.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".