Innovation in insecticide discovery: Approaches to the discovery of new classes of insecticides
Bibliographic record
Abstract
The continuing demand for agrochemical insecticides that can meet increasing grower, environmental, consumer and regulatory requirements creates the need for the development of new solutions for managing crop pest insects. The development of resistance to the currently available insecticidal products adds another critical driver for new insecticidal active ingredients (AIs). One avenue to meeting these challenges is the creation of new classes of insecticidal molecules to act as starting points and prototypes stimulating further spectrum, efficacy and environmental impact refinements. A new class of insecticides is foreshadowed by the first molecule exemplifying that class (first-in-class, FIC) and offers one measure of innovation within the agrochemical industry. Most insecticides owe their discovery to competitor-inspired (i.e. competitor patents/products) or next-generation (follow-on to a company's pre-existing product) strategies. In contrast, FIC insecticides primarily emerge from a bioactive hypothesis approach, with the largest segment resulting from the exploration of new areas of chemistry/heterocycles and underexploited motifs. Natural products also play an important role in the discovery of FIC insecticides. Understanding the origins of these FIC compounds and the approaches used in their discovery can provide insights into successful strategies for future FIC insecticides. This review analyses information on historic and recently introduced FIC insecticides. Its main objective has been to identify the most successful discovery strategies for identifying new agrochemical solutions to meet the challenge of minimizing crop losses resulting from insects. © 2022 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".