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ÇĽ╦! îdocutils.nodesöîdocumentöôö)üö}ö(î rawsourceöî öîchildrenö]öh îsectionöôö)üö}ö(hhh]ö(h îtitleöôö)üö}ö(hîSWhat is drug repurposing, virtual screening and drug-target interaction prediction?öh]öh îTextöôöîSWhat is drug repurposing, virtual screening and drug-target interaction prediction?öůöüö}ö(hhîparentöhhhîsourceöNîlineöNubaî attributesö}ö(îidsö]öîclassesö]öînamesö]öîdupnamesö]öîbackrefsö]öuîtagnameöhhh hhhîK/Users/futianfan/Downloads/spring2020/DeepPurpose/docs/source/notes/DTI.rstöh Kubh )üö}ö(hhh]ö(h)üö}ö(hîDrug Repurposingöh]öhîDrug Repurposingöůöüö}ö(hh0hh.hhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)hhh+hhhh*h Kubh î paragraphöôö)üö}ö(hîCDrug repurposing aims to repivot an existing drug to a new therapy.öh]öhîCDrug repurposing aims to repivot an existing drug to a new therapy.öůöüö}ö(hh@hh>hhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)h<hh*h Khh+hhubeh }ö(h ]öîdrug-repurposingöah!]öh#]öîdrug repurposingöah%]öh']öuh)h hh hhhh*h Kubh )üö}ö(hhh]ö(h)üö}ö(hîVirtual Screeningöh]öhîVirtual Screeningöůöüö}ö(hhYhhWhhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)hhhThhhh*h K ubh îdefinition_listöôö)üö}ö(hhh]öh îdefinition_list_itemöôö)üö}ö(hîťVirtual screening means to use computer software to automatically screen a huge space of potential drug-target pairs to obtain a predicted binding score. öh]ö(h îtermöôö)üö}ö(hîHVirtual screening means to use computer software to automatically screenöh]öhîHVirtual screening means to use computer software to automatically screenöůöüö}ö(hhthhrubah }ö(h ]öh!]öh#]öh%]öh']öuh)hphh*h Khhlubh î definitionöôö)üö}ö(hhh]öh=)üö}ö(hîPa huge space of potential drug-target pairs to obtain a predicted binding score.öh]öhîPa huge space of potential drug-target pairs to obtain a predicted binding score.öůöüö}ö(hhçhhůubah }ö(h ]öh!]öh#]öh%]öh']öuh)h<hh*h Khhéubah }ö(h ]öh!]öh#]öh%]öh']öuh)hÇhhlubeh }ö(h ]öh!]öh#]öh%]öh']öuh)hjhh*h Khhgubah }ö(h ]öh!]öh#]öh%]öh']öuh)hehhThhhh*h Nubeh }ö(h ]öîvirtual-screeningöah!]öh#]öîvirtual screeningöah%]öh']öuh)h hh hhhh*h K ubh )üö}ö(hhh]ö(h)üö}ö(hîDrug-Target Interactionöh]öhîDrug-Target Interactionöůöüö}ö(hh▓hh░hhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)hhhşhhhh*h Kubh=)üö}ö(hXŁ Both of these tasks are able to save cost, time, and facilitate drug discovery. Deep learning has shown strong performance in repurposing and screening. It relies on the accurate and fast prediction of a fundamental task: drug-target interaction prediction. DTI prediction task aims to predict the input drug target pairÔÇÖs interaction probability or binding score. Given a powerful DTI model that is able to generalize over a new unseen dataset, we can then extend to repurposing/screening. For repurposing, given a new target of interest, we can first pair it to a repurposing drug library. Then this list of input drug-target pairs is fed into the trained DTI model, which will output the predicted binding score. Similarly, for virtual screening, given a list of screening drug-target pairs we want, the DTI model can output the predicted interaction binding scores. We can then rank the predicted outcome based on their binding scores and test the top-k options in the wet lab after manual inspection. DeepPurpose automates this process. By only requiring one line of code, it aggregates five pretrained deep learning models and retrieves a list of ranked potential outcomes.öh]öhXŁ Both of these tasks are able to save cost, time, and facilitate drug discovery. Deep learning has shown strong performance in repurposing and screening. It relies on the accurate and fast prediction of a fundamental task: drug-target interaction prediction. DTI prediction task aims to predict the input drug target pairÔÇÖs interaction probability or binding score. Given a powerful DTI model that is able to generalize over a new unseen dataset, we can then extend to repurposing/screening. For repurposing, given a new target of interest, we can first pair it to a repurposing drug library. Then this list of input drug-target pairs is fed into the trained DTI model, which will output the predicted binding score. Similarly, for virtual screening, given a list of screening drug-target pairs we want, the DTI model can output the predicted interaction binding scores. We can then rank the predicted outcome based on their binding scores and test the top-k options in the wet lab after manual inspection. DeepPurpose automates this process. By only requiring one line of code, it aggregates five pretrained deep learning models and retrieves a list of ranked potential outcomes.öůöüö}ö(hh└hhżhhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)h<hh*h Khhşhhubh=)üö}ö(hXÁ Identifying Drug-Target Interactions (DTI) will greatly narrow down the scope of search of candidate medications, and thus can plays a pivotal role in drug discovery. Drugs usually interact with one or more proteins to achieve their functions. However, discovering novel interactions between drugs and target proteins is crucial for the development of new drugs, since the aberrant expression of proteins may cause side effects of drugs.öh]öhXÁ Identifying Drug-Target Interactions (DTI) will greatly narrow down the scope of search of candidate medications, and thus can plays a pivotal role in drug discovery. Drugs usually interact with one or more proteins to achieve their functions. However, discovering novel interactions between drugs and target proteins is crucial for the development of new drugs, since the aberrant expression of proteins may cause side effects of drugs.öůöüö}ö(hh╬hh╠hhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)h<hh*h K,hhşhhubh=)üö}ö(hXA Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this project, our goal is to focus on deep learning approaches for drug-target interaction (DTI) prediction.öh]öhXA Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this project, our goal is to focus on deep learning approaches for drug-target interaction (DTI) prediction.öůöüö}ö(hh▄hh┌hhhNh Nubah }ö(h ]öh!]öh#]öh%]öh']öuh)h<hh*h K6hhşhhubeh }ö(h ]öîdrug-target-interactionöah!]öh#]öîdrug-target interactionöah%]öh']öuh)h hh hhhh*h Kubeh }ö(h ]öîQwhat-is-drug-repurposing-virtual-screening-and-drug-target-interaction-predictionöah!]öh#]öîSwhat is drug repurposing, virtual screening and drug-target interaction prediction?öah%]öh']öuh)h hhhhhh*h Kubah }ö(h ]öh!]öh#]öh%]öh']öîsourceöh*uh)hîcurrent_sourceöNî current_lineöNîsettingsöîdocutils.frontendöîValuesöôö)üö}ö(hNî generatoröNî datestampöNî source_linköNî source_urlöNî toc_backlinksöîentryöîfootnote_backlinksöKî sectnum_xformöKîstrip_commentsöNîstrip_elements_with_classesöNî strip_classesöNî report_levelöKî halt_levelöKîexit_status_levelöKîdebugöNîwarning_streamöNî tracebacköłîinput_encodingöî utf-8-sigöî input_encoding_error_handleröîstrictöîoutput_encodingöîutf-8öî output_encoding_error_handleröj îerror_encodingöîUTF-8öî error_encoding_error_handleröîbackslashreplaceöî language_codeöîenöîrecord_dependenciesöNîconfigöNî id_prefixöhîauto_id_prefixöîidöî dump_settingsöNîdump_internalsöNîdump_transformsöNîdump_pseudo_xmlöNîexpose_internalsöNîstrict_visitoröNî_disable_configöNî_sourceöh*î _destinationöNî _config_filesö]öîpep_referencesöNî pep_base_urlöî https://www.python.org/dev/peps/öîpep_file_url_templateöîpep-%04döîrfc_referencesöNî rfc_base_urlöî https://tools.ietf.org/html/öî tab_widthöKî trim_footnote_reference_spaceöëîfile_insertion_enabledöłî raw_enabledöKîsyntax_highlightöîlongöî smart_quotesöłîsmartquotes_localesö]öî character_level_inline_markupöëîdoctitle_xformöëî docinfo_xformöKîsectsubtitle_xformöëîembed_stylesheetöëîcloak_email_addressesöłîenvöNubîreporteröNîindirect_targetsö]öîsubstitution_defsö}öîsubstitution_namesö}öîrefnamesö}öîrefidsö}öînameidsö}ö(h§h˛hQhNh¬hžhÝhŕuî nametypesö}ö(h§NhQNh¬NhÝNuh }ö(h˛h hNh+hžhThŕhşuî footnote_refsö}öî citation_refsö}öî autofootnotesö]öîautofootnote_refsö]öîsymbol_footnotesö]öîsymbol_footnote_refsö]öî footnotesö]öî citationsö]öîautofootnote_startöKîsymbol_footnote_startöK î id_counteröî collectionsöîCounteröôö}öůöRöîparse_messagesö]öîtransform_messagesö]öî transformeröNî decorationöNhhub.