Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers

CONTEXT

Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies.

METHODS

To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis.

Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches

The role of NLRP3 inflammasome in innate immunity is newly recognized. The NLRP3 protein is a family of nucleotide-binding and oligomerization domain-like receptors as well as a pyrin domain-containing protein. It has been shown that NLRP3 may contribute to the development and progression of a variety of diseases, such as multiple sclerosis, metabolic disorders, inflammatory bowel disease, and other auto-immune and auto-inflammatory conditions. The use of machine learning methods in pharmaceutical research has been widespread for several decades. An important objective of this study is to apply machine learning approaches for the multinomial classification of NLRP3 inhibitors. However, data imbalances can affect machine learning. Therefore, a synthetic minority oversampling technique (SMOTE) has been developed to increase the sensitivity of classifiers to minority groups. The QSAR modelling was performed using 154 molecules retrieved from the ChEMBL database (version 29). The accuracy of the multiclass classification top six models was found to fall within ranges of 0.99 to 0.86, and log loss ranges of 0.2 to 2.3, respectively. The results showed that the receiver operating characteristic curve (ROC) plot values significantly improved when tuning parameters were adjusted and imbalanced data was handled. Moreover, the results demonstrated that SMOTE offers a significant advantage in handling imbalanced datasets as well as substantial improvements in overall accuracy of machine learning models. The top models were then used to predict data from unseen datasets. In summary, these QSAR classification models exhibited robust statistical results and were interpretable, which strongly supported their use for rapid screening of NLRP3 inhibitors.