Innovation in insecticide discovery: Approaches to the discovery of new classes of insecticides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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