ChemVassa: A New Method for Identifying Small Molecule Hits in Drug Discovery
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
ChemVassa, a new chemical structure search technology, was developed to allow rapid in silico screening of compounds for hit and hit-to-lead identification in drug development. It functions by using a novel type of molecular descriptor that examines, in part, the structure of the small molecule undergoing analysis, yielding its "information signature." This descriptor takes into account the atoms, bonds, and their positions in 3-dimensional space. For the present study, a database of ChemVassa molecular descriptors was generated for nearly 16 million compounds (from the ZINC database and other compound sources), then an algorithm was developed that allows rapid similarity searching of the database using a query molecular descriptor (e.g., the signature of atorvastatin, below). A scoring metric then allowed ranking of the search results. We used these tools to search a subset of drug-like molecules using the signature of a commercially successful statin, atorvastatin (Lipitor™). The search identified ten novel compounds, two of which have been demonstrated to interact with HMG-CoA reductase, the macromolecular target of atorvastatin. In particular, one compound discussed in the results section tested successfully with an IC50 of less than 100uM and a completely novel structure relative to known inhibitors. Interactions were validated using computational molecular docking and an Hmg-CoA reductase activity assay. The rapidity and low cost of the methodology, and the novel structure of the interactors, suggests this is a highly favorable new method for hit generation.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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