Botanical drugs: a new strategy for structure-based target prediction
Why this work is in the frame
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Bibliographic record
Abstract
Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it