Bioactivity Prediction

Bioactivity prediction aims to computationally determine the biological effects of molecules, primarily to accelerate drug discovery. Current research heavily utilizes deep learning, particularly graph neural networks and Siamese networks, often incorporating techniques like chemical language processing and topological data analysis to learn molecular representations from various data sources, including large-scale structural datasets and substrate scope information. These advancements are improving the accuracy and efficiency of predicting bioactivity, enabling more effective virtual screening and potentially reducing the time and cost associated with drug development. Furthermore, efforts are focused on addressing data heterogeneity and sparsity to enhance the robustness and generalizability of predictive models.