Molecular Descriptor

Molecular descriptors are numerical representations of chemical structures, aiming to capture relevant physicochemical and topological features for predicting molecular properties and behaviors. Current research emphasizes the use of graph neural networks and generative models like variational autoencoders (VAEs) and Generative Flow Networks (GFlowNets), often combined with other machine learning techniques like multiple linear regression and ensemble methods, to improve prediction accuracy and handle diverse datasets. This field is crucial for accelerating drug discovery, materials science, and environmental science by enabling faster and more efficient screening of compounds and prediction of their properties, reducing reliance on expensive and time-consuming experimental methods. The development of robust and explainable models is a key focus to enhance the reliability and interpretability of predictions.