Investigation of Classical Organic and Ionic Liquid Cosolvents for Early-Stage Screening in Fragment-Based Inhibitor Design with Unrelated Bacterial and Human Dihydrofolate Reductases
Bibliographic record
Abstract
Drug design by methods such as fragment screening requires effective solubilization of millimolar concentrations of small organic compounds while maintaining the properties of the biological target. We investigate four organic solvents and three 1-butyl-3-methylimidazolium (BMIm)-based ionic liquids (ILs) as cosolvents to establish conditions for screening two structurally unrelated dihydrofolate reductases (DHFRs) that are prime drug targets. Moderate concentrations (10%-15%) of cosolvents had little effect on inhibition of the microbial type II R67 DHFR and of human DHFR (hDHFR), while higher concentrations of organic cosolvents generally decreased activity of both DHFRs. In contrast, a specific IL conserved the activity of one DHFR, while severely reducing the activity of the other, and vice versa, illustrating the differing effect of ILs on distinct protein folds. Most of the cosolvents investigated preserved the fold of R67 DHFR and had little effect on binding of the cofactor NADPH, but reduced the productive affinity for its substrate. In contrast, cosolvents resulted in modest structural destabilization of hDHFR with little effect on productive affinity. We conclude that the organic cosolvents, methanol, dimethylformamide, and dimethylsulfoxide, offer the most balanced conditions for early-stage compound screening as they maintain sufficient biological activity of both DHFRs while allowing for compound dissolution in the millimolar range. However, IL cosolvents showed poor capacity to solubilize organic compounds at millimolar concentrations, mitigating their utility in early-stage screening. Nonetheless, ILs could provide an alternative to classical organic cosolvents when low concentrations of inhibitors are used, as when characterizing higher affinity inhibitors.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".