Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing

Sansare, Sameera; Duran, Tibo; Mohammadiarani, Hossein; Goyal, Manish; Yenduri, Gowtham; Costa, António Pedro; Xu, Xiaoming; O’Connor, Thomas; Burgess, Diane J.; Chaudhuri, Bodhisattwa

doi:10.1016/j.ijpharm.2021.120713

Cited by 20 publications

(14 citation statements)

References 55 publications

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“…The numerical values of descriptors could quantitatively describe the physical and chemical information of molecules and are the basis of QSPR and Quantitative Structure–Activity Relationship (QSAR) studies, such as those used in ANN to predict certain properties of molecules or to elucidate the interactions between molecules and their surroundings. PaDEL descriptors are open-source software that could be used to compute 1D–3D molecular descriptors, integrating 1875(1444 1D&2D descriptors and 431 3D descriptors) descriptors and 12 molecular fingerprints. − …”

Section: Methods and Modelingmentioning

confidence: 99%

Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

Yue

2023

Crystal Growth & Design

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Among the physical properties characterizing cocrystals, melting temperature is one of the primary properties. Its prediction has been done by researchers, but in the known prediction models, there are some limitations. In order to adapt to the requirements of data development further and improve the quality of prediction, two prediction models of cocrystal melting temperature, the artificial neural network (ANN(I)) and the message passing neural network (MPNN), are proposed in this paper with molecular descriptors and molecular graphs as the inputs. As for ANN(I), molecular descriptors are applied to the prediction of cocrystal melting temperature for the first time, and the importance of features is evaluated by SHapley Additive explanation. For dataset 1 established in this paper, the prediction accuracy of ANN(I) is 95.75% on the training set and 95.67% on the test set. The feature evaluation of ANN(I) reveals that GATS2v, AMR, BCUTc-1h, MLFER_BH, BCUTp-1l, and ATSC1c play major roles in predicting the melting temperature. The ANN(I) model based on molecular descriptors could explain the relationship between the molecular structure and melting temperature to some extent, but its prediction accuracy is relatively poor. Thus, the message passing neural network (MPNN), a deep learning model based on molecular graphs, is proposed. For the test sets of two datasets used in this paper, the prediction accuracy of MPNN is 99.84 and 99.63%, respectively. Also, in the data pre-processing, the MPNN extracts the structural features of molecules through molecular graphs, which could save time and workload a lot. The excellent performance suggests that the MPNN model might be a more suitable tool for the prediction of cocrystal properties in the future.

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Section: Methods and Modelingmentioning

confidence: 99%

Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

Yue

2023

Crystal Growth & Design

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“…Considering CMAs and critical process parameters, Sansare et al [120] used two types of BP neural networks to improve the liposomes preparation process: a multi-input multi-output (MIMO) and multi-input single-output (MISO) model. For the MIMO model, an ANN was used to construct all relationships.…”

Section: Selection and Optimization Of Formulationsmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

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During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

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“…Chemoinformatics is also essential for the development of new drugs [8][9][10][11] and for the assessment of their toxicity [12]. Since the databases of chemicals are extensive and count in millions of elements, the approach of using artificial intelligence [13], artificial neural networks [14], and machine learning seems to be the only method to deal with such a huge amount of collected information [15].…”

Section: Introductionmentioning

confidence: 99%

Model of an Artificial Blastula for Assessing Development Toxicity

Muzika¹,

Górecki²

2024

Biomedical Engineering

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We are concerned with computer simulations of a ring of 20 coupled CSTRs with glycolytic oscillatory reaction. Each CSTR represents an artificial cell, and the ring can be regarded as an artificial blastula. The cells are coupled to two adjacent CSTRs via the mass exchange of reagents. The glycolytic oscillatory reaction is simulated using the two-variable core model. Our work is focused on the classification of stationary discrete nonuniform concentration patterns (discrete Turing patterns). The control parameters in simulations are autocatalytic and inhibition rate coefficients, as well as the transport rate coefficients. We performed the analysis of stability and bifurcations of stationary states to identify the stationary states. The inflow of reagents into each CSTR was used to initiate a particular pattern. We propose a method to assess the morphogenetic toxicity of any chemical from a database by switching between patterns or between patterns and oscillations. Moreover, we investigated nonuniform patterns that create discrete concentration waves inside the ring of 20 coupled cells, which can trigger gastrulation.

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Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing

Cited by 20 publications

References 55 publications

Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Model of an Artificial Blastula for Assessing Development Toxicity

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