Computational methods for drug target profiling and polypharmacology
Bibliographic record
Abstract
The ‘magic bullet’ concept of hitting a target responsible for a disease with a drug molecule tailored to act as a selective agent, has been a therapeutic goal since the beginning of drug research and one of the driving forces in modern drug discovery for several decades. With the rise of structural biology and molecular pharmacology, and the shift from in vivo to in vitro models in the initial evaluation of biological effects of molecules, the aim of obtaining absolute target specificity had become a goal that seemed within reach. However, there is evidence that drugs interact with many physiological targets, and that polypharmacology bears essential importance on therapeutic efficacy. In this light, discovering compounds exhibiting the ‘right’ selectivity profile (i.e., interaction with several targets or target hubs in a converging biological pathway) has become the holy grail in drug development. Recent examples in the kinase field illustrate this new paradigm. Whereas imatinib (Gleevec®; Novartis, Switzerland) and sunitinib (Sutent®; Pfizer, NY, USA) were designed to be selective, later they were found to be more promiscuous than initially thought [1,2], which could explain why these molecules are successful therapeutically. As recently pointed out, searching for selectively nonselective kinase inhibitors when striking the right balance, can deliver candidates and drugs with superior efficacy compared with inhibitors with high specificity for a single kinase [3].
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".