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dc.contributor.authorLiu, Shengchao
dc.date.accessioned2018-09-20T16:22:54Z
dc.date.available2018-09-20T16:22:54Z
dc.date.issued2018-09-20T16:22:54Z
dc.identifier.citationTR1854
dc.identifier.urihttp://digital.library.wisc.edu/1793/78768
dc.description.abstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We start by considering a wide range of ligand-based machine learning and docking-based approaches for virtual screening, and present a strategy for choosing which algorithm is best for prospective compound prioritization. During this process, we find that input information may affect the model performance. Thus we emphasize the impacts of different levels of molecule representation and introduce N-gram graph, a novel representation for a molecular graph. N-gram graph on traditional machine learning models is able to reach the state-of-the-art performance. Another issue we observe is that multi-task learning can negatively impact the performance on some individual tasks. We propose a reinforced multi-task learning (RMTL) framework, and preliminary results show that RMTL can address the issue in the two-task cases.en
dc.relation.ispartofseriestechnical report;TR1854
dc.subjectdeep learningen
dc.subjectgraph representationen
dc.subjectreinforcement learningen
dc.subjectmulti-task learningen
dc.subjectnegative transferen
dc.titleExploration on Deep Drug Discovery: Representation and Learningen
dc.typeTechnical Reporten


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    Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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