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Fast visualizing and profiling your de novo generated library.
The main objective of drug discovery is to find a molecule with desired biological properties. De novo drug design is one of the most promising and scalable approach with the advances of deep learning techniques. Recently, a number of architectures for molecular generation, such as recurrent neural networks (RNN), variational autoencoders (VAE), and generative adversarial networks (GANs) have been developed and proven successful in generating target-focus molecule library. Successful cases demonstrate that the deep learning based de novo molecular design could accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created great demand for the visualization and functional profiling for the generated molecules.
DenovoProfiling, a webserver dedicated for de novo generated molecule library profiling. We aim to provide a user-friendly public webserver to support the structure and chemical space visualization, ADMET prediction, molecular alignment, drugs profiling, targets and pathway profiling. We integrated cheminformatics tools and databases to provide comprehensive annotations for the de novo generated molecules. We believe that DenovoProfiling could be an efficient tool for user to capture the knowledge of de novo generated molecules quickly.