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Active Search for Computer‐aided Drug Design
Author(s) -
Oglic Dino,
Oatley Steven A.,
Macdonald Simon J. F.,
Mcinally Thomas,
Garnett Roman,
Hirst Jonathan D.,
Gärtner Thomas
Publication year - 2018
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201700130
Subject(s) - drug discovery , markov chain , computational biology , docking (animal) , probabilistic logic , a priori and a posteriori , chemical space , in silico , computer science , drug design , machine learning , biology , artificial intelligence , bioinformatics , biochemistry , medicine , philosophy , nursing , epistemology , gene
We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data‐driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an α v integrin, the target protein that belongs to a group of Arg−Gly−Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determinedα v β 6protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays.