Abstract
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.
Original language | English |
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Pages (from-to) | 253-258 |
Number of pages | 6 |
Journal | Computers in Biology and Medicine |
Volume | 100 |
DOIs | |
Publication status | Published - 1 Sept 2018 |
Externally published | Yes |
Keywords
- Deep learning
- DUD-E
- Graph convolution
- MUV
- Neural fingerprint
- PDBBind
- Virtual screening