Synthesis, Antiproliferative Effect and In Silico LogP Prediction of BIM-23052 Analogs Containing Tyr Instead of Phe.

Autor: Danalev, Dancho¹ (AUTHOR) ddanalev@uctm.edu, Iliev, Ivan² (AUTHOR) taparsky@abv.bg, Dobrev, Stefan³ (AUTHOR) sdobrev@iom.bas.bg, Angelova, Silvia³ (AUTHOR) sea@iomt.bas.bg, Petrin, Stoiko¹ (AUTHOR), Dzimbova, Tatyana⁴ (AUTHOR) tania_dzimbova@abv.bg, Ivanova, Elena² (AUTHOR) elena9512@abv.bg, Borisova, Dessislava⁵ (AUTHOR) dborissova@yahoo.com, Naydenova, Emilia⁵ (AUTHOR) emilia@uctm.edu

Publikováno v: Pharmaceutics. Apr2023, Vol. 15 Issue 4, p1123. 14p.

A comparison of molecular representations for lipophilicity quantitative structure–property relationships with results from the SAMPL6 logP Prediction Challenge.

Autor: Lui, Raymond¹ (AUTHOR), Guan, Davy¹ (AUTHOR), Matthews, Slade¹ (AUTHOR) slade.matthews@sydney.edu.au

Publikováno v: Journal of Computer-Aided Molecular Design. May2020, Vol. 34 Issue 5, p523-534. 12p.

Synthesis, Antiproliferative Effect and In Silico LogP Prediction of BIM-23052 Analogs Containing Tyr Instead of Phe

Autor: Dancho Danalev, Ivan Iliev, Stefan Dobrev, Silvia Angelova, Stoiko Petrin, Tatyana Dzimbova, Elena Ivanova, Dessislava Borisova, Emilia Naydenova

Publikováno v: Pharmaceutics, Vol 15, Iss 4, p 1123 (2023)

(1) Background: Hydrophobicity (or lipophilicity) is a limiting factor in the ability of molecules to pass through cell membranes and to perform their function. The ability to efficiently access cytosol is especially important when a synthetic compou

Externí odkaz: https://doaj.org/article/d82a6f044e184e70acf75b75b677df3e

MRlogP: Transfer Learning Enables Accurate logP Prediction Using Small Experimental Training Datasets

Autor: Steven Shave, Manfred Auer, Yan-Kai Chen

Publikováno v: Processes
Volume 9
Issue 11
Processes, Vol 9, Iss 2029, p 2029 (2021)
Chen, Y-K, Shave, S & Auer, M 2021, ' MRlogP : Transfer learning enables accurate logP prediction using small experimental training datasets ', Processes, vol. 9, no. 11, 2029 . https://doi.org/10.3390/pr9112029

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72a60ed3cc894a622e292342bd94ffc9

Use of AI for the development of two new early drug discovery techniques: deep and transfer learning for LogP prediction and dimensionality reduction for sequence-based virtual screening

Autor: Chen, Yan-Kai

Prediction of small molecule physiochemical properties and their biological targets is extremely valuable in the effort to reduce costs and attrition rates within drug discovery. In-silico techniques are now routinely used to guide medicinal chemistr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65de9fc617ceb77908c2cc41d21ec7fe
https://hdl.handle.net/1842/40373