Irina Oshchepkova
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Articles
Recent activity by Irina Oshchepkova-
Percepta: Early assessment of chemical space coverage for a compound set
DescriptionBefore investing in a compound set, teams may want to know how diverse it is in terms of basic properties. Percepta predictions can be used to generate a quick view of chemical space cov...
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Percepta: Quick comparison of analog series using radar (spider) plots
DescriptionVisual comparison of several predicted properties across a small series of analogs can be done via radar (spider) plots. Even if plotted in an external tool, Percepta predictions provide...
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Percepta: Using predicted solubility and logD to flag potential precipitation risk
DescriptionPrecipitation risk in biological fluids (e.g., GI tract, plasma) depends on solubility and local supersaturation. Percepta predictions can help identify compounds that may be at higher c...
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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...