Irina Oshchepkova
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Articles
Recent activity by Irina Oshchepkova-
Percepta: Using predicted ionization and lipophilicity to anticipate LC method modes
DescriptionChoice of LC mode (reversed‑phase, HILIC, ion‑exchange) often depends on compound polarity and ionization behavior. Percepta predictions can guide which modes are most promising to try.S...
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Percepta: Using predictions for quick triage of legacy compounds
DescriptionOlder internal compound collections may have limited experimental ADME/Tox data. Percepta predictions can support quick triage of which legacy compounds might be worth revisiting for new...
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Percepta: Rough prioritization of compounds for inhalation vs oral routes
DescriptionSome programs consider multiple administration routes (e.g., inhalation vs oral). While Percepta does not model route‑specific PK directly, basic properties (solubility, lipophilicity, v...
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Percepta: Pre‑screening virtual analogs for “rule‑of‑five” and related filters
DescriptionIn many projects, basic oral “drug‑likeness” filters (such as Lipinski’s rule‑of‑five variants) are applied to virtual analogs before synthesis. Percepta property predictions can support...
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Percepta: Qualitative use of predictions when designing soft‑drug candidates
DescriptionSoft drugs are designed to be active locally but rapidly inactivated systemically (e.g., via metabolism). Percepta’s predictions for metabolic stability, clearance, and lipophilicity can...
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Percepta: Screening fragment‑like vs lead‑like properties in early design
DescriptionEarly discovery often differentiates “fragment‑like” molecules from “lead‑like” or “drug‑like” ones based on simple property ranges. Percepta predictions can help classify proposed struc...
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Percepta: Integrating predictions into early risk/benefit discussions
DescriptionPhysChem Property and ADME/Tox predictions can inform early risk/benefit assessments for candidate compounds. Used carefully, they help structure discussions among medicinal chemists, DM...
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Percepta: Using predicted properties to anticipate food‑effect risk conceptually
DescriptionOral drugs can show different exposure with and without food. While Percepta does not model food effects directly, predicted solubility, lipophilicity, and ionization can contribute to a...
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Percepta: Using predicted properties to guide prodrug design ideas
DescriptionProdrugs are often designed to improve solubility, permeability, or stability. Percepta predictions can help evaluate whether a prodrug candidate may address property issues of the paren...
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Percepta: Using property predictions to prioritize analogs for synthesis
DescriptionIn early design stages, many virtual analogs may be proposed. Percepta predictions help narrow down the list to compounds more likely to meet project property criteria.SolutionGenerate a...