Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Lipid nanoparticle(LNP) is commonly used to deliver mRNA vaccines.Currently,LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time.Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines.Firstly,325 data samples of mRNA vaccine LNP formulations with IgG titer were collected.The machine learning algorithm,lightGBM,was used to build a prediction model with good performance(R^(2)>0.87).More importantly,the critical substructures of ionizable lipids in LNPs were identified by the algorithm,which well agreed with published results.The animal experimental results showed that LNP using DLin-MC3-DMA(MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102,which was consistent with the model prediction.Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment.The result showed that the lipid molecules aggregated to form LNPs,and mRNA molecules twined around the LNPs.In summary,the machine learning predictive model for LNP-based mRNA vaccines was first developed,validated by experiments,and further integrated with molecular modeling.The prediction model can be used for virtual screening of LNP formulations in the future.

Prediction and validation of tonal noise from underwater propellers

Prediction and validation of low-frequency line spectrum noise from ship propeller under non-cavitating condition is presented.The flow field is analyzed with potential-based panel method,which requires the hydrodynamic forces to be integrated over the actual blade surface,rather than over the mean-chord surface.Then the pressure data is used as the input for Ffowcs Williams-Hawkings formulation to predict the far field acoustics.At the same time,propeller unsteady force is measured in hull-behind condition in China Large Cavitation Channel（CLCC）.Line spectrum noise of the 1st blade passage frequency（BPF） of a five-bladed propeller operating in a non-uniform flow field is got according to the calculated and measured unsteady forces,in which good agreement is obtained,and the 1st BPF noise difference is within 3.0 dB.The investigation reveals that prediction precision of the propeller＇s 1st BPF unsteady force with panel method have reached engineering practical degree,providing significant parameters for prediction of propeller line spectrum noise.

Effect of Rotor-stator Axial Gap on the Tip Seal Leakage Flow of a Steam Turbine

The tip leakage flow in different axial gaps is numerically investigated based on the Reynolds-Averaged Navier-Stokes equations and k-ω SST turbulent model using commercial CFD software ANSYS Fluent.The characteristics of vortexes in the flow field of tip seal and the distribution law of rotor tip leakage loss are analyzed under the change of axial gap.The results show that the flow field in the blade tip seal undergoes complex changes with the increase of axial gap.There are two opposite vortices in the seal cavity with small axial gap,namely,the large vortex in the cavity and the small vortex near the wall of the shroud.The small vortex gradually disappears with the increase of the axial gap.When the axial gap is increased to a certain value,the reverse large vortex is formed in the cavity behind the low teeth of seal,and the leakage rate increases significantly;the steam flow angle and the mixing loss also show different changes.Based on the traditional calculation formula of tip leakage rate,a more accurate calculation formula of tip leakage rate is constructed by introducing equivalent labyrinth seal teeth to consider the variation of axial gap.