Impact of natural products on discovery of, and innovation in, crop protection compounds
Bibliographic record
Abstract
Natural products (NPs) have long been an important source of, and inspiration for, developing novel compounds to control weeds, pathogens and insect pests. In this review, we use a dataset of 800 historic, current and emerging crop protection compounds to explore the influence of NPs on the introduction of new crop protection compounds (fungicides, herbicides, insecticides) as a function of time. NPs, their semisynthetic derivatives (NPDs) and compounds inspired by NPs (NP mimics, NPMs) account for 17% of all crop protection compounds. NPs, NPDs, and NPMs have been a fairly constant source of new agrochemicals over the past 70 years. NP synthetic equivalents (NPSEs) is a fourth group of NP-related crop protection compounds composed of synthetic compounds which by chance also happen to have an NP model (but are not involved in the discovery). If NPSE compounds are also included, then 50% of all crop protection compounds hypothetically could have had a NP origin. Similar trends also hold true for the impact of NPs on the discovery of new modes of action (MoA) or innovation in crop protection compounds as measured by the number of first-in-class compounds. NPs have had the largest impact on the numbers and global sales (2018 USD) of insecticides compared to fungicides and herbicides. The present analysis highlights NPs as a long-standing and continuing source of new chemistry, new MoAs and innovation in crop protection compound discovery. © 2021 Society of Chemical Industry.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".