The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products
Bibliographic record
Abstract
Covering: up to the end of December, 2018 There are still a disturbing number of incorrect natural product structure elucidations reported in the literature. The use of Computer-Assisted Structure Elucidation (CASE) programs can minimize this risk by generating all structures that are consistent with the input data and by ranking these structures in order of probability. They can successfully determine structures for complex natural products, with the possible exception of compounds with very few protons. Current CASE programs utilize mainly 2D COSY and HMBC correlation data for structure generation with a starting assumption that all observed peaks are due to pairs of atoms no more than 3 bonds apart. We discuss these assumptions and the problems that occur when they are violated. We also discuss the advantages and disadvantages of other types of 2D data that could be included at the structure generation stage. Four different CASE programs are described with particular emphasis on how they deal with the presence of longer range correlation peaks. These programs provide only planar skeletal structures. However, a new program that relies on different types of stereospecific NMR data to determine 3D structures is also described. Other types of computer assistance for structure elucidation are discussed, including the increasing use of theoretical DFT calculations to determine 3D structures and to predict chemical shifts. Finally, we suggest possible improvements in these programs and suggest that a challenge match between the developers of current CASE programs would be useful.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".