Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City 11829, Egypt.
Computer Science Department, Shaqra University, Shaqra City 11961, Saudi Arabia.
Molecules. 2023 Jan 23;28(3):1125. doi: 10.3390/molecules28031125.
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.
分子图像经常被用于教育和合成探索中，以预测分子特性。深度学习（DL）也对药物研究产生了影响，例如对细胞图像的解释以及开发有机分子合成的创新方法。尽管这些领域的研究意义重大，但对 DL 在药物开发中的应用进行全面综述将超出单个综述的范围。在这项研究中，我们将集中讨论 DL 影响分子设计的一个主要领域：使用机器学习（ML）预测改性吉妥单抗的分子特性。人工智能和机器学习技术在药物研究和开发中至关重要。换句话说，深度学习（DL）算法和人工神经网络（ANN）改变了这一领域。简而言之，人工智能和机器学习的进步为合理药物设计和探索提供了良好的潜力，最终将使人类受益。在本文中，长短期记忆（LSTM）被用于将改性吉妥单抗的 SMILE 转换为分子图像。然后，二维分子表示及其立即可见的亮点应该提供足够的数据来预测原子设计的次要特征。我们旨在使用 K-均值聚类来找到改性吉妥单抗的性质；类似于 RNN 的 ML 工具。为了支持这一假设，本研究中使用并评估了基于 AI 图片的神经网络（NN）聚类。通过深度学习开发的新型化学物质的特性预测已久。因此，带有 RNN 的 LSTM 允许我们预测改性吉妥单抗分子的性质。所提出模型的总准确率为 98.68%。改性吉妥单抗研究中分子性质预测的准确率具有足够的预测外推和泛化能力。本研究中提出的模型仅需几秒钟或几分钟即可计算，因此比现有技术更快、更有效。简而言之，机器学习可以成为预测改性吉妥单抗分子性质的有用工具。
