Exploring neurotherapeutic space: how many neurological drugs exist (or could exist)?
Bibliographic record
Abstract
OBJECTIVES: Since high-throughput screening of compound libraries (virtual or real) against druggable targets is increasingly being used to discover therapies for brain disorders, it is crucial to ascertain if such screening methods adequately explore 'neurotherapeutic space (i.e. the total number of molecules that are or could be neuroactive)'. We present an approach to providing an estimate of the size of neurotherapeutic space. METHODS: Molecular modelling and statistical calculations were used to determine the number of molecules, which exist or could exist, with the necessary physicochemical and structural properties to be neurologically active drugs. KEY FINDINGS: Assuming eight fundamental types of drug-receptor interactions, five different functional groups per type of interaction and five different molecular platforms for each functional group array, we calculated the total number of molecules that could be contained within a 7 Å radius sphere, used to define neuroactive chemical space. This calculation revealed that there are 6 × 10(15) molecules that could be neurological drugs. CONCLUSIONS: Clearly, when it comes to exploring neurochemical space, we are still in our infancy and conventional high-throughput screening provides only a very limited sampling of the neuroactive chemical space that is available to neurotherapeutic compounds.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".