Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs.

Predicting plasma protein binding (PPB) is crucial in drug development due to its profound impact on drug efficacy and safety. In our study, we employed a convolutional neural network (CNN) as a tool to extract valuable information from the molecular structures of 100 different drugs. These extracted features were then used as inputs for a feedforward network to predict the PPB of each drug. Through this approach, we successfully obtained 10 specific numerical features from each drug's molecular structure, which represent fundamental aspects of their molecular composition. Leveraging the CNN's ability to capture these features significantly improved the precision of our predictions. Our modeling results revealed impressive accuracy, with an R2 train value of 0.89 for the training dataset, a [Formula: see text] of 0.98, a [Formula: see text] of 0.931 for the external validation dataset, and a low cross-validation mean squared error (CV-MSE) of 0.0213. These metrics highlight the effectiveness of our deep learning techniques in the fields of pharmacokinetics and drug development. This study makes a substantial contribution to the expanding body of research exploring the application of artificial intelligence (AI) and machine learning in drug development. By adeptly capturing and utilizing molecular features, our method holds promise for enhancing drug efficacy and safety assessments in pharmaceutical research. These findings underscore the potential for future investigations in this exciting and transformative field.

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain.

Purpose: The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. Methods: A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set's external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). Results: The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. Conclusion: The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model's accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

Characterization of a novel biosurfactant produced by Staphylococcus sp. strain 1E with potential application on hydrocarbon bioremediation.

A biosurfactant-producing bacterium (Staphylococcus sp. strain 1E) was isolated from an Algerian crude oil contaminated soil. Biosurfactant production was tested with different carbon sources using the surface tension measurement and the oil displacement test. Olive oil produced the highest reduction in surface tension (25.9 dynes cm(-1)). Crude oil presented the best substrate for 1E biosurfactant emulsification activity. The biosurfactant produced by strain 1E reduced the growth medium surface tension below 30 dynes cm(-1). This reduction was also obtained in cell-free filtrates. Biosurfactant produced by strain 1E showed stability in a wide range of pH (from 2 to 12), temperature (from 4 to 55 °C) and salinity (from 0 to 300 g l(-1)) variations. The biosurfactant produced by strain 1E belonged to lipopeptide group and also constituted an antibacterial activity againt the pathogenic bacteria such as Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis. Phenanthrene solubility in water was enhanced by biosurfactant addition. Our results suggest that the 1E biosurfactant has interesting properties for its application in bioremediation of hydrocarbons contaminated sites.