Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220767539> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4220767539 abstract "Calculating the free energy of drug permeation across membranes carries great importance in pharmaceutical and related applications. Traditional methods, including experiments and molecular simulations, are expensive and time-consuming, and existing statistical methods suffer from low accuracy. In this work, we propose a hybrid approach that combines molecular dynamics simulations and deep learning techniques to predict the free energy of permeation of small drug-like molecules across lipid membranes with high accuracy and at a fraction of the computational cost of advanced sampling methods like umbrella sampling. We have performed several molecular dynamics simulations of molecules in water and lipid bilayers to obtain multidimensional time-series data of features. Deep learning architectures based on Long Short-Term Memory networks, attention mechanisms, and dense layers are built to estimate free energy from the time series data. The prediction errors for the test set and an external validation set are much lower than that of existing data-driven approaches, with R2 of the best model around 0.99 and 0.82 for the two cases. Our approach reduces the time required for free energy calculations by an order of magnitude. This work presents an attractive option for high-throughput virtual screening of molecules based on their membrane permeabilities, demonstrates the applicability of language processing techniques in biochemical problems, and suggests a novel way of integrating physics with statistical learning to great success" @default.
- W4220767539 created "2022-04-03" @default.
- W4220767539 creator A5014980959 @default.
- W4220767539 creator A5027795850 @default.
- W4220767539 creator A5036288812 @default.
- W4220767539 creator A5047680471 @default.
- W4220767539 date "2022-03-25" @default.
- W4220767539 modified "2023-09-26" @default.
- W4220767539 title "Deep Learning Models for the Estimation of Free Energy of Permeation of Small Molecules across Lipid Membranes" @default.
- W4220767539 doi "https://doi.org/10.26434/chemrxiv-2022-h2rw7" @default.
- W4220767539 hasPublicationYear "2022" @default.
- W4220767539 type Work @default.
- W4220767539 citedByCount "0" @default.
- W4220767539 crossrefType "posted-content" @default.
- W4220767539 hasAuthorship W4220767539A5014980959 @default.
- W4220767539 hasAuthorship W4220767539A5027795850 @default.
- W4220767539 hasAuthorship W4220767539A5036288812 @default.
- W4220767539 hasAuthorship W4220767539A5047680471 @default.
- W4220767539 hasBestOaLocation W42207675391 @default.
- W4220767539 hasConcept C105795698 @default.
- W4220767539 hasConcept C106131492 @default.
- W4220767539 hasConcept C108583219 @default.
- W4220767539 hasConcept C127413603 @default.
- W4220767539 hasConcept C140779682 @default.
- W4220767539 hasConcept C147597530 @default.
- W4220767539 hasConcept C154945302 @default.
- W4220767539 hasConcept C177264268 @default.
- W4220767539 hasConcept C185592680 @default.
- W4220767539 hasConcept C186060115 @default.
- W4220767539 hasConcept C186370098 @default.
- W4220767539 hasConcept C18762648 @default.
- W4220767539 hasConcept C199360897 @default.
- W4220767539 hasConcept C31972630 @default.
- W4220767539 hasConcept C33923547 @default.
- W4220767539 hasConcept C41008148 @default.
- W4220767539 hasConcept C41625074 @default.
- W4220767539 hasConcept C50670333 @default.
- W4220767539 hasConcept C55493867 @default.
- W4220767539 hasConcept C59593255 @default.
- W4220767539 hasConcept C78519656 @default.
- W4220767539 hasConcept C86803240 @default.
- W4220767539 hasConceptScore W4220767539C105795698 @default.
- W4220767539 hasConceptScore W4220767539C106131492 @default.
- W4220767539 hasConceptScore W4220767539C108583219 @default.
- W4220767539 hasConceptScore W4220767539C127413603 @default.
- W4220767539 hasConceptScore W4220767539C140779682 @default.
- W4220767539 hasConceptScore W4220767539C147597530 @default.
- W4220767539 hasConceptScore W4220767539C154945302 @default.
- W4220767539 hasConceptScore W4220767539C177264268 @default.
- W4220767539 hasConceptScore W4220767539C185592680 @default.
- W4220767539 hasConceptScore W4220767539C186060115 @default.
- W4220767539 hasConceptScore W4220767539C186370098 @default.
- W4220767539 hasConceptScore W4220767539C18762648 @default.
- W4220767539 hasConceptScore W4220767539C199360897 @default.
- W4220767539 hasConceptScore W4220767539C31972630 @default.
- W4220767539 hasConceptScore W4220767539C33923547 @default.
- W4220767539 hasConceptScore W4220767539C41008148 @default.
- W4220767539 hasConceptScore W4220767539C41625074 @default.
- W4220767539 hasConceptScore W4220767539C50670333 @default.
- W4220767539 hasConceptScore W4220767539C55493867 @default.
- W4220767539 hasConceptScore W4220767539C59593255 @default.
- W4220767539 hasConceptScore W4220767539C78519656 @default.
- W4220767539 hasConceptScore W4220767539C86803240 @default.
- W4220767539 hasLocation W42207675391 @default.
- W4220767539 hasOpenAccess W4220767539 @default.
- W4220767539 hasPrimaryLocation W42207675391 @default.
- W4220767539 hasRelatedWork W2032307681 @default.
- W4220767539 hasRelatedWork W2088523430 @default.
- W4220767539 hasRelatedWork W2260580338 @default.
- W4220767539 hasRelatedWork W2299214707 @default.
- W4220767539 hasRelatedWork W2315698829 @default.
- W4220767539 hasRelatedWork W2582566781 @default.
- W4220767539 hasRelatedWork W2618322139 @default.
- W4220767539 hasRelatedWork W2902398019 @default.
- W4220767539 hasRelatedWork W3047613627 @default.
- W4220767539 hasRelatedWork W3122864963 @default.
- W4220767539 isParatext "false" @default.
- W4220767539 isRetracted "false" @default.
- W4220767539 workType "article" @default.