KNIME-based Analysis of Off-Target Effect of Drugs Related to The Molecular 2D Fingerprint
Bibliographic record
Abstract
PURPOSE: Structurally similar molecules are likely to have similar biological activity. In this study, similarity searching based on molecular 2D fingerprint was performed to analyze off-target effects of drugs. The purpose of this study is to determine the correlation between the adverse effects and drug off-targets. METHODS: A workflow was built using KNIME to run dataset preparation of twenty-nine targets from ChEMBL, generate molecular 2D fingerprints of the ligands, calculate the similarity between ligand sets, and compute the statistical significance using similarity ensemble approach (SEA). Tanimoto coefficients (Tc) are used as a measure of chemical similarity in which the values between 0.2 and 0.4 are the most common for the majority of ligand pairs and considered to be insignificant similar. RESULT: The majority of ligand sets are unrelated, as is evidenced by the intrinsic chemical differences and the classification of statistical significance based on expectation value. The rank-ordered expectation value of inter-target similarity showed a correlation with off-target effects of the known drugs. CONCLUSION: Similarity-searching using molecular 2D fingerprint can be applied to predict off-targets and correlate them to the adverse effects of the drugs. KNIME as an open-source data analytic platform is applicable to build a workflow for data mining of ChEMBL database and generating SEA statistical model.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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 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".