Prediction of Hydroxylation Rate Constants from Molecular Structure by Cheminformatics Methods and Computational Neural Networks

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1182/thumbnail.jp

Artificial intelligence and chemical kinetics enabled property-oriented fuel design for internal combustion engine

Fuel Genome Project aims at addressing the forward problem of fuel property prediction and the inverse problems of molecule design, retrosynthesis and reaction condition prediction. This work primarily addresses the forward problem by integrating feature engineering theory, artificial intelligence (AI) technologies, gas-phase chemical kinetics. Group contribution method (GCM) is utilized to establish the GCM-UOB (University of Birmingham) 1.0 system with 22 molecular descriptors and the surrogate formulation is to minimize the difference of functional group fragments between target fuel and surrogate. The improved QSPR (quantitative structure–activity relationship)-UOB 2.0 system with 32 molecular features couples with machine learning (ML) algorithms to establish the regression models for fuel ignition quality prediction. QSPR-UOB 3.0 scheme expands to 42 molecular descriptors to improve the molecular resolution of aromatics and specific fuel types. The obtained structural features combining with ML algorithms enable to predict 15 physicochemical properties with high fidelity and efficiency. In addition to the technical route of ML-QSPR models, another route of deep learning-convolution neural network (DL-CNN) is proposed for property prediction and yield sooting index (YSI) is taken as a case study. The predicted accuracy of DL-CNN is inferior to the ML-QSPR model at its current status, but its benefit of automated feature extraction and rapid advance in classification problem make it a promising solution for regression problem. A high-throughput fuel screening is performed to identify the molecules with desired properties for both spark ignition (SI) and compression ignition (CI) engines which contains the Tier 1 physicochemical properties screening (based on the ML-QSPR models) and Tier 2 chemical kinetic screening (based on the detailed chemical mechanisms). Polyoxymethylene dimethyl ether 3 (PODE3) and diethoxymethane (DEM) are promising carbon-neutral fuels for CI engines and they are recommended by the virtual screening results. Their ignition delay time, laminar flame speed and dominant reactions of PODE3 and DEM are examined by chemical kinetics and a new DEM mechanism including both low and high-temperature reactions is constructed. Concluding remarks and research prospects are summarized in the final section

ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY

Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also

Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils

The efficiency of four modeling approaches, namely group contributions, corresponding-states principle, σ-moment-based neural networks, and graph machines, are compared for the estimation of the surface tension (ST) of 269 pure liquid compounds at 25 °C from their molecular structure. This study focuses on liquids containing only carbon, oxygen, hydrogen or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. Neural network estimations are performed from σ-moment descriptors as defined in the COSMO-RS model, while methods based on group contributions, corresponding-states principle and graph machines use 2D molecular information (SMILES codes). The graph machine approach provides the best results, estimating the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl myristate or decamethylcyclopentasiloxane (D5), with accuracy better than 1 mN.m–1. A demonstration of the graph machine model using the recent Docker technology is available for download in the Supplementary Information

Structure-based Generalized Models for Selected Pure-fluid Saturation Properties

This study focused on developing generalized structure-based models for predicting pure-fluid surface tensions and saturation viscosities. Reliable experimental data for a wide range of molecular species were assembled from the DIPPR physical property database. The Scaled-Variable-Reduced-Coordinate (SVRC) framework was used to correlate the available data for the saturation properties under consideration. Quantitative Structure-Property Relationships (QSPR) was used to generalize the SVRC model parameters. Non-linear QSPR models involving a hybrid of Genetic Algorithms (GA) and Artificial Neural Networks (ANN) were developed for the model parameters. Specifically, the SVRC-QSPR models, in general, were found to be capable of providing generalized a priori predictions for surface tension and saturation viscosities with an absolute average deviation (AAD) of approximately 2% using end-point input data.School of Chemical Engineerin

SYNTHESIS, MOLECULAR MODELING, AND QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP STUDIES OF UNDEC-10-ENEHYDRAZIDE DERIVATIVES AS ANTIMICROBIAL AGENTS

Objective: In recent years, an increasing frequency and severity of antimicrobial resistance to different antimicrobial agents, demands new remedies for the treatment of infections. Therefore, in this study, a series of undec-10-enehydrazide derivatives were synthesized and screened for in vitro activity against selected pathogenic microbial strains.Methods: The synthesis of the intermediate and target compounds was performed by standard procedure. Synthesized compounds were screened for antimicrobial activity by tube dilution method. Molecular docking study of synthesized derivatives was also performed to find out their interaction with the target site of β-ketoacyl-acyl carrier protein synthase III, (FabH; pdb id:3IL7) by docking technique. Quantitative structure–activity relationship (QSAR) studies were also performed to correlate antimicrobial activity with structural properties of synthesized molecules.Results: Antimicrobial screening results showed that compound 8 having benzylidine moiety with methoxy groups at meta and para position and compound 16 having 3-chloro-2-(3-flourophenyl)-4-oxoazetidine moiety was found to be most potent. QSAR studies revealed the importance of Randic topology parameter (R) in describing the antimicrobial activity of synthesized derivatives. Molecular docking study indicated hydrophobic interaction of deeply inserted aliphatic side chain of the ligand with FabH. The N-atoms of hydrazide moiety interacts with Ala246 and Asn247 through H-bonding. The m- and p-methoxy groups form H-bond with water and side chain of Arg36, respectively.Conclusion: Compound 8 having benzylidine moiety with methoxy groups at meta and para position and compound 16 having 3-chloro-2-(3- flourophenyl)-4-oxoazetidine moiety was found to most potent antibacterial and antifungal compounds, respectively

Prediction of properties of new halogenated olefins using two group contribution approaches

AbstractThe increasingly restrictive regulations for substances with high ozone depletion and global warming potentials are driving the search for new sustainable fluids with low environmental impact. Recent research works have pointed out the great potential of fluorine- and chlorine-based olefins as refrigerants and solvents, due to their environmentally-friendly features. However there is a lack of experimental data of their thermophysical properties. In this work we present two models based on a group contribution method, using a classical approach and neural networks, to predict the critical temperature, critical pressure, normal boiling temperature, acentric factor, and ideal gas heat capacity of organic fluids containing chlorine and/or fluorine. The accuracy of the prediction capacity of the two models is analyzed, and compared with equivalent methods in the literature. The models showed an average reduction of the absolute relative deviation for all the studied properties of more than 50%, compared to other methods. In addition, it was observed that the neural-network-based model yielded a better accuracy than the classical approach in the prediction of all the properties, except for the acentric factor, due to the lack of experimental data for this property

Predicting physical properties of alkanes with neural networks

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods