Biofiltration Process for Treatment of Water and Wastewater

Sinha, Prashant; Mukherji, Suparna

doi:10.1007/s41403-022-00360-0

Cited by 7 publications

(1 citation statement)

References 79 publications

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“…Various prediction techniques, including Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Partial Least Squares (PLS), Artificial Neural Network (ANN), K-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and RF, are utilized to forecast the properties of molecules. These predictions rely on descriptors that capture the characteristics of chemical structures [9][10][11].…”

Section: Literature Review (Literatür Taraması)mentioning

confidence: 99%

Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms

Gider,

Budak

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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The effective development and design of pharmaceuticals hold fundamental importance in the fields of medicine and the pharmaceutical industry. In this process, the accurate prediction of drug molecule solubility is a critical factor influencing the bioavailability, pharmacokinetics, and toxicity of drugs. Traditionally, mathematical equations based on chemical and physical properties have been used for drug solubility prediction. However, in recent years, with the advancement of artificial intelligence and machine learning techniques, new approaches have been developed in this field. This study evaluated different modeling approaches consisting of Graph Neural Networks (GNN), Multilayer Perceptron (MLP), and traditional Machine Learning (ML) algorithms. The Random Forest (RF) model stands out as the optimal performer, manifesting superior efficacy through the attainment of minimal error rates. It attains a Root Mean Square Error (RMSE) value of 1.2145, a Mean Absolute Error (MAE) value of 0.9221, and an R-squared (R2) value of 0.6575. In contrast, GNN model displays comparatively suboptimal performance, as evidenced by an RMSE value of 1.8389, an MAE value of 1.4684, and an R2 value of 0.2147. These values suggest that the predictions of this model contain higher errors compared to other models, and its explanatory power is lower. These findings highlight the performance differences among different modeling approaches in drug solubility prediction. The RF model is shown to be more effective than other methods, while the GNN model performs less effectively. This information provides valuable insights into which model should be preferred in pharmaceutical design and development processes.

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Section: Literature Review (Literatür Taraması)mentioning

confidence: 99%