Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

Abstract

Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the R 2 values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.

Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

“…13–15 Machine learning has the ability to predict almost every property for which enough data are available. 16–18…”

“…[13][14][15] Machine learning has the ability to predict almost every property for which enough data are available. [16][17][18] Sanchez-Lengeling et al reported a machine learning approach based on the Bayesian method to predict Hansen solubility parameters. Various types of inputs such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculated properties were used as inputs.…”

Designing of benzofuran‐based monomers for photodetectors through similarity analysis and library enumeration

“…ML employs algorithms for data visualization and analysis, and it has numerous advantages over traditional programming methods. ML has proven useful in a variety of fields, including transcription and image identification 34 …”

“…ML has proven useful in a variety of fields, including transcription and image identification. 34 In the present study, to design new building block units, benzofuran-based blocks are used as a standard. Benzo[1,2-b:4,5-b 0 ]dithiophene (BDT) has been extensively used to design polymers for photovoltaics applications; however, benzo[1,2-b:4,5-b 0 ]difuran (BDF) has rarely been utilized in photovoltaic applications.…”

Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

“…The data science technology known as machine learning (ML) employs new methods and formulas to solve complicated issues that are challenging to write using conventional programming techniques [24][25][26][27]. The ML analysis is based on collecting sufficient trustworthy material data (either experimental or computational) that best describes the behavior or quality of the material or its applications to create models for finding new materials without having to repeat the same experiments or calculations [28,29].…”