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Interaction prediction in structure-based virtual screening using deep learning
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DOI：10.1016/j.compbiomed.2017.09.007.png)
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Abstract
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We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each molecule separately. These fingerprints are further non-linearly transformed, their inner product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E, MUV and PDBBind databases.
Keywords:
Virtual screening
Neural fingerprint
Graph convolution
Deep learning
PDBBind
DUD-E
MUV
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NUCLEIC ACIDS RESEARCH
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NUCLEIC ACIDS RESEARCHIF13.1
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