Application of SMILES strings to identification of functional groups responsible for biological activity in medicinal compounds
Bibliographic record
Abstract
An efficient and practical approach to identification of important functional groups in the structure of medicinal molecules that are main factor to create biological activity by use of SMILES line notation system is described. Simplicity, high proficiency and fast timing are the main of current method. In this study we aim to find an association between some of the identified functional groups, using SMILES code and their corresponding biological properties in the Canada Drug database. In this study, each functional group and its category which has been tested is presented in the corresponding number of occurrences in the category and the total number is shown as well. The p-value for each functional group – category is calculated using proportion test and R statistical software. The tabular results, the last column indicates the impact of our hypothesis for example, sulfonylurea and 5-thio-1H-tetrazole functional groups are associated with their corresponding category and are significant at 0.05 level. Penicillin and 3-aminopropane-1,2-diol are also significant in the majority of their categories. we have developed a method to create a logical and robust relationship between functional groups and biological activity of molecules. According to existing protocol, finding functional groups responsible for the biological activity of medicinal or chemical compounds is possible. Biological Activity, Functional Group, Medicine, SMILES.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".