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Deep learning models are inspired by information processing and communication patterns in biological nervous systems. Deep learning is a part of the machine learning and is applied successfully in a wild range of areas, such as computer vision, computer games, natural language processing, and self-driving cars. Deep neural networks possess multiple hidden layers and are capable of computing layers of adaptive non-linear features that capture increasingly complex data patterns with each additional layer.
Deep learning based virtual screening
Deep learning may be particularly well-suited for data mining in the life sciences because this approach deals with complex patterns in nature, systems biology and heterogeneous “big data”. Recent advances in deep learning have made significant contributions to biological science and drug discovery. Previous studies suggested that deep learning techniques have shown superior performance to other machine learning algorithms in virtual screening, which is a critical step to accelerate the drug discovery.
DeepScreening is a user-friendly web server for constructing deep learning models using public dataset or user provided dataset, which would help biologist and chemist virtual screening the chemical probes or drugs for a specific target of interest. With DeepScreening, user could conveniently construct a deep learning model and generate target focused de novo libraries. The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or chemical libraries in stock, in which the synthetic compounds, natural products, drugs, covalent agents, PPI and allosteric modulators are collected from various chemical vendors. From deep models training to virtual screening, and target focused de novo library generation, all those tasks could be finished with DeepScreening. We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery. More details could be found in the publication.