Molecular Property Prediction of Modified Gedunin Using Machine Learning

Abstract

Images of molecules are often utilized in education and synthetic exploration to predict molecular characteristics. Deep learning (DL) has also had an influence on drug research, such as the interpretation of cellular images as well as the development of innovative methods for the synthesis of organic molecules. Although research in these areas has been significant, a comprehensive review of DL applications in drug development would be beyond the scope of a single Account. In this study, we will concentrate on a single major area where DL has influenced molecular design: the prediction of molecular properties of modified gedunin using machine learning (ML). AI and ML technologies are critical in drug research and development. In these other words, deep learning (DL) algorithms and artificial neural networks (ANN) have changed the field. In short, advances in AI and ML present a good potential for rational drug design and exploration, which will ultimately benefit humanity. In this paper, long short-term memory (LSTM) was used to convert a modified gedunin SMILE into a molecular image. The 2D molecular representations and their immediately visible highlights should then provide adequate data to predict the subordinate characteristics of atom design. We aim to find the properties of modified gedunin using K-means clustering; RNN-like ML tools. To support this postulation, neural network (NN) clustering based on the AI picture is used and evaluated in this study. The novel chemical developed via profound learning has long been predicted on characteristics. As a result, LSTM with RNNs allow us to predict the properties of molecules of modified gedunin. The total accuracy of the suggested model is 98.68%. The accuracy of the molecular property prediction of modified gedunin research is promising enough to evaluate extrapolation and generalization. The model suggested in this research requires just seconds or minutes to calculate, making it faster as well as more effective than existing techniques. In short, ML can be a useful tool for predicting the properties of modified gedunin molecules.

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

“…In the assessment of facial recognition expression systems, it's imperative to evaluate their performance across multiple metrics to ensure their effectiveness. Four key metrics commonly utilized for this purpose are Accuracy, Precision, Recall, and F1-Score [44][45][46][47][48].…”

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

“…Classification plays a crucial role in the medical field [35][36][37], and this manuscript specifically focuses on T2W images from the dataset. The MR images of the BraTS type do not require any preprocessing steps such as head stripping or noise removal.…”

Cyberattack Detection in Wireless Sensor Networks using a Hybrid Feature Reduction Technique with AI and Machine Learning Methods

“…Furthermore, an IDS must have a low or zero percentage of false alarms in addition to detecting threats. Hence, the suggested model's performance is evaluated based on four important parameters, namely: Accuracy (ACY), Recall (RE), Precision (PRE), and F1-Score (FS) [37][38][39]. The strategy for evaluating the four metric parameters is represented by the following equations.…”