Gonczarek A, Tomczak JM, Zaręba S, Kaczmar J, Dąbrowski P, Walczak MJ. Interaction prediction in structure-based virtual screening using deep learning.
Comput Biol Med 2017;
100:253-258. [PMID:
28941550 DOI:
10.1016/j.compbiomed.2017.09.007]
[Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 08/22/2017] [Accepted: 09/08/2017] [Indexed: 12/29/2022]
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.
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