removed input_old

input_old/README.md

-# Projekt CiS-Projekt 2021/22
-Input-Skripts
-

input_old/example_urls

-https://pubs.acs.org/doi/10.1021/acs.jcim.5b00332
-https://pubs.acs.org/doi/10.1021/acs.jcim.6b00709

input_old/input_fj.py

-#!/usr/bin/env python3
-"""
-Functions for information retrieval of articles from the ACS journal JCIM
-
-"""
-
-__author__ = "Florian Jochens"
-__email__ = "fj@andaco.de"
-__status__ = "Production"
-#__copyright__ = ""
-#__credits__ = ["", "", "", ""]
-#__license__ = ""
-#__version__ = ""
-#__maintainer__ = ""
-
-from bs4 import BeautifulSoup as bs
-import requests as req
-import sys  
-from pathlib import Path
-
-class Publication:
-    #_registry = []
-    _citations = []
-    _references = []
-    
-    def __init__(self, title, publication_date, contributors, doi_url, 
-                 subjects = None, num_citations = None):
-        #self._registry.append(self)
-        self.title = title
-        self.publication_date = publication_date
-        self.contributors = contributors
-        self.doi_url = doi_url
-        self.subjects = subjects
-        self.num_citations = num_citations
-        #self._citations = []
-        #self._references = []
-
-class Citation:
-    def __init__(self, title, journal, contributors, doi_url):
-        self.title = title
-        self.journal = journal
-        self.contributors = contributors
-        self.doi_url = doi_url
-
-class References:
-    def __init__(self, title, journal, contributors, doi_url):
-        self.title = title
-        self.journal = journal
-        self.contributors = contributors
-        self.doi_url = doi_url
-    
-def get_article_info(soup):
-    header = soup.find('div', class_ = 'article_header-left pull-left')
-    article_title = header.find('span', class_ = 'hlFld-Title').text
-    publication_date = header.find('span', class_ = 'pub-date-value').text
-    for link in header.find('div', class_ = 'article_header-doiurl'):
-        doi_url = link.get('href')
-    subs = header.find('div', class_ = 'article_header-taxonomy')
-    subjects = []
-    for sub in subs.find_all('a'):
-        subjects.append(sub.get('title'))
-    cons = header.find('ul', class_ = 'loa')
-    contributors = []
-    for con in cons.find_all('span', class_ = 'hlFld-ContribAuthor'):
-        contributors.append(con.text)
-    numc = header.find('div', class_ = 'articleMetrics_count')
-    if not numc.a:
-        num_citations = 0
-    else:
-        num_citations = numc.a.text
-
-    pub = Publication(article_title, publication_date, contributors, doi_url,
-                      subjects, num_citations)
-    return pub
-
-def get_download_url():
-    export = soup.find('div', class_ = 'cit-download-dropdown_content')
-    url = 'https://pubs.acs.org'
-    for link in export.find_all('a'):
-        if link.get('title') == 'Citation and references':
-            url += link.get('href')     
-    print(url)
-    return url
-
-def download(url): # Download citation and references file
-    if url.find('='):
-        filename = url.rsplit('=', 1)[1]
-    path = Path(('./files/' + filename))
-    if path.is_file():
-        print("File already exists")
-    else:
-        print("File does not exist")
-
-def get_citation_info(pub, num_citations, soup):
-    pub._citations = []
-    details = soup.find('ol', class_ = 'cited-content_cbyCitation')
-    titles = [] 
-    for title in details.find_all('span', 
-            class_ = 'cited-content_cbyCitation_article-title'):
-        titles.append(title.text.replace('.', ''))
-    journal_names = []
-    for name in details.find_all('span',
-            class_ = 'cited-content_cbyCitation_journal-name'):
-        journal_names.append(name.text)
-    doi_urls = []
-    for url in details.find_all('a'):
-        doi_urls.append(url.get('href'))
-    contributors = []
-    for contrib in details.find_all('span', 
-            class_ = 'cited-content_cbyCitation_article-contributors'):
-        contributors.append(contrib.text)
-    for i in range(0, int(num_citations)):
-        pub._citations.append(Citation(titles[i], journal_names[i], 
-                              contributors[i], doi_urls[i]))
-def print_pub_info(pub):
-    print(f'''Article title:    {pub.title}
-Publication date: {pub.publication_date}
-DOI-URL:          {pub.doi_url}
-
-Subjects:''')
-    print(*(pub.subjects), sep = ", ")
-    print('\nContributors:')
-    print(*(pub.contributors), sep = ", ")
-
-    if int(pub.num_citations) > 0:
-        if int(pub.num_citations) == 1:
-            print(f'\nThis publication is cited by the following publication:\n')
-        else:
-            print(f'\nThis publication is cited by the following {pub.num_citations} publications:\n')
-        for citation in pub._citations:
-            print(f'''
-    Title:        {citation.title}
-    Journal:      {citation.journal}
-    Contributors: {citation.contributors}
-    DOI-URL:      {citation.doi_url}
-            ''')
-    else:
-        print('\nThis publication is not cited by any other publication.')
-
-def input(url):
-    html_text = req.get(url).text
-    soup = bs(html_text, 'html.parser')
-    
-    pub = get_article_info(soup)
-    if int(pub.num_citations) > 0:
-        get_citation_info(pub, int(pub.num_citations), soup)
-    return pub
-
-#if len(sys.argv) != 2:
-#    sys.stderr.write('Usage: {} <url>\n'.format(sys.argv[0]))
-#    exit(1)
-#url = sys.argv[1]
-#pub = input(url)
-#print_pub_info(pub)

input_old/pub.py

-class Publication:
-    #_registry = []
-    #_citations = []
-    #_references = []
-    
-    def __init__(self, title, publication_date, contributors, doi_url, 
-                 subjects, num_citations):
-        #self._registry.append(self)
-        self.title = title
-        self.publication_date = publication_date
-        self.contributors = contributors
-        self.doi_url = doi_url
-        self.subjects = subjects
-        self.num_citations = num_citations
-        self.num_references = num_references
-    	self._citations = []
-    	self._references = []
-
-class Citation:
-    def __init__(self, title, journal, contributors, doi_url):
-        self.title = title
-        self.journal = journal
-        self.contributors = contributors
-        self.doi_url = doi_url
-
-class References:
-    def __init__(self, title, journal, contributors, doi_url):
-        self.title = title
-        self.journal = journal
-        self.contributors = contributors
-        self.doi_url = doi_url
-

input_old/test.py

-#!/usr/bin/env python3
-
-from input_fj import input, print_pub_info
-import sys
-
-if len(sys.argv) != 3:
-    sys.stderr.write('Usage: {} <url> <url>\n'.format(sys.argv[0]))
-    exit(1)
-url = sys.argv[1]
-url2 = sys.argv[2]
-pub = input(url)
-print_pub_info(pub)
-pub2 = input(url2)
-print_pub_info(pub2)
-

input_old/x

-Article title:    Feasibility of Active Machine Learning for Multiclass Compound Classification
-Publication date: January 7, 2016
-DOI-URL:          https://doi.org/10.1021/acs.jcim.5b00332
-
-Subjects:
-Algorithms, Molecules, Drug discovery, Screening assays, Receptors
-
-Contributors:
-Tobias Lang, Florian Flachsenberg, Ulrike von Luxburg, Matthias Rarey
-
-This publication is cited by the following 30 publications:
-
-
-    Title:        Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery 
-    Journal:      Chemical Reviews
-    Contributors: Xin Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, Shengyong Yang. 
-    DOI-URL:      https://doi.org/10.1021/acs.chemrev.8b00728
-            
-
-    Title:        De Novo Molecule Design by Translating from Reduced Graphs to SMILES 
-    Journal:      Journal of Chemical Information and Modeling
-    Contributors: Peter Pogány, Navot Arad, Sam Genway, Stephen D. Pickett. 
-    DOI-URL:      https://doi.org/10.1021/acs.jcim.8b00626
-            
-
-    Title:        Designing Algorithms To Aid Discovery by Chemical Robots 
-    Journal:      ACS Central Science
-    Contributors: Alon B. Henson, Piotr S. Gromski, Leroy Cronin. 
-    DOI-URL:      https://doi.org/10.1021/acscentsci.8b00176
-            
-
-    Title:        Modeling Kinase Inhibition Using Highly Confident Data Sets 
-    Journal:      Journal of Chemical Information and Modeling
-    Contributors: Sorin Avram, Alina Bora, Liliana Halip, Ramona Curpăn. 
-    DOI-URL:      https://doi.org/10.1021/acs.jcim.7b00729
-            
-
-    Title:        Predictive Models for Fast and Effective Profiling of Kinase Inhibitors 
-    Journal:      Journal of Chemical Information and Modeling
-    Contributors: Alina  Bora, Sorin  Avram, Ionel  Ciucanu, Marius  Raica, and Stefana  Avram  . 
-    DOI-URL:      https://doi.org/10.1021/acs.jcim.5b00646
-            
-
-    Title:        Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery 
-    Journal:      Bioinformatics
-    Contributors: Huangqingbo  Sun, Robert F  Murphy, . 
-    DOI-URL:      https://doi.org/10.1093/bioinformatics/btab322
-            
-
-    Title:        An Artificial Intelligence Approach Based on Hybrid CNN-XGB Model to Achieve High Prediction Accuracy through Feature Extraction, Classification and Regression for Enhancing Drug Discovery in Biomedicine 
-    Journal:      International Journal of Biology and Biomedical Engineering
-    Contributors: Mukesh  Madanan, Biju T.  Sayed, Nurul Akhmal  Mohd Zulkefli, Nitha C.  Velayudhan. 
-    DOI-URL:      https://doi.org/10.46300/91011.2021.15.22
-            
-
-    Title:        Artificial Intelligence in Medicinal Chemistry 
-    Journal:      
-    Contributors: Edward  Griffen, Alexander  Dossetter, Andrew  Leach, Shane  Montague. 
-    DOI-URL:      https://doi.org/10.1002/0471266949.bmc267
-            
-
-    Title:        Practical Chemogenomic Modeling and Molecule Discovery Strategies Unveiled by Active Learning 
-    Journal:      
-    Contributors: J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.1016/B978-0-12-801238-3.11533-8
-            
-
-    Title:        Machine learning phases and criticalities without using real data for training 
-    Journal:      Physical Review B
-    Contributors: D.-R.  Tan, F.-J.  Jiang. 
-    DOI-URL:      https://doi.org/10.1103/PhysRevB.102.224434
-            
-
-    Title:        Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure–activity patterns in NCI-60 cell lines 
-    Journal:      RSC Medicinal Chemistry
-    Contributors: Takumi  Nakano, Shunichi  Takeda, J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.1039/D0MD00110D
-            
-
-    Title:        Active learning efficiently converges on rational limits of toxicity prediction and identifies patterns for molecule design 
-    Journal:      Computational Toxicology
-    Contributors: Ahsan  Habib Polash, Takumi  Nakano, Christin  Rakers, Shunichi  Takeda, J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.1016/j.comtox.2020.100129
-            
-
-    Title:        Practical considerations for active machine learning in drug discovery 
-    Journal:      Drug Discovery Today: Technologies
-    Contributors: Daniel  Reker. 
-    DOI-URL:      https://doi.org/10.1016/j.ddtec.2020.06.001
-            
-
-    Title:        Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration 
-    Journal:      Molecular Systems Design & Engineering
-    Contributors: Bowen  Li, Srinivas  Rangarajan. 
-    DOI-URL:      https://doi.org/10.1039/C9ME00078J
-            
-
-    Title:        Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification 
-    Journal:      Molecules
-    Contributors: Ahsan Habib  Polash, Takumi  Nakano, Shunichi  Takeda, J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.3390/molecules24152716
-            
-
-    Title:        Capturing and applying knowledge to guide compound optimisation 
-    Journal:      Drug Discovery Today
-    Contributors: Matthew  Segall, Tamsin  Mansley, Peter  Hunt, Edmund  Champness. 
-    DOI-URL:      https://doi.org/10.1016/j.drudis.2019.02.004
-            
-
-    Title:        A novel graph kernel on chemical compound classification 
-    Journal:      Journal of Bioinformatics and Computational Biology
-    Contributors: Qiangrong  Jiang, Jiajia  Ma. 
-    DOI-URL:      https://doi.org/10.1142/S0219720018500269
-            
-
-    Title:        Accelerating Drug Discovery Using Convolution Neural Network Based Active Learning 
-    Journal:      
-    Contributors: Pengfei  Liu, Kwong-Sak  Leung. 
-    DOI-URL:      https://doi.org/10.1109/TENCON.2018.8650298
-            
-
-    Title:        An Adaptive Lightweight Security Framework Suited for IoT 
-    Journal:      
-    Contributors: Menachem  Domb. 
-    DOI-URL:      https://doi.org/10.5772/intechopen.73712
-            
-
-    Title:        Adaptive mining and model building of medicinal chemistry data with a multi-metric perspective 
-    Journal:      Future Medicinal Chemistry
-    Contributors: JB  Brown. 
-    DOI-URL:      https://doi.org/10.4155/fmc-2018-0188
-            
-
-    Title:        Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families 
-    Journal:      ChemMedChem
-    Contributors: Christin  Rakers, Rifat Ara  Najnin, Ahsan Habib  Polash, Shunichi  Takeda, J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.1002/cmdc.201700677
-            
-
-    Title:        Automating drug discovery 
-    Journal:      Nature Reviews Drug Discovery
-    Contributors: Gisbert  Schneider. 
-    DOI-URL:      https://doi.org/10.1038/nrd.2017.232
-            
-
-    Title:        Classifiers and their Metrics Quantified 
-    Journal:      Molecular Informatics
-    Contributors: J. B.  Brown. 
-    DOI-URL:      https://doi.org/10.1002/minf.201700127
-            
-
-    Title:        Active Search for Computer-aided Drug Design 
-    Journal:      Molecular Informatics
-    Contributors: Dino  Oglic, Steven A.  Oatley, Simon J. F.  Macdonald, Thomas  Mcinally, Roman  Garnett, Jonathan D.  Hirst, Thomas  Gärtner. 
-    DOI-URL:      https://doi.org/10.1002/minf.201700130
-            
-
-    Title:        Selection of Informative Examples in Chemogenomic Datasets 
-    Journal:      
-    Contributors: Daniel  Reker, J. B.  Brown. 
-    DOI-URL:      https://doi.org/10.1007/978-1-4939-8639-2_13
-            
-
-    Title:        The value of prior knowledge in machine learning of complex network systems 
-    Journal:      Bioinformatics
-    Contributors: Dana  Ferranti, David  Krane, David  Craft, . 
-    DOI-URL:      https://doi.org/10.1093/bioinformatics/btx438
-            
-
-    Title:        Lightweight adaptive Random-Forest for IoT rule generation and execution 
-    Journal:      Journal of Information Security and Applications
-    Contributors: Menachem  Domb, Elisheva  Bonchek-Dokow, Guy  Leshem. 
-    DOI-URL:      https://doi.org/10.1016/j.jisa.2017.03.001
-            
-
-    Title:        Active learning for computational chemogenomics 
-    Journal:      Future Medicinal Chemistry
-    Contributors: Daniel  Reker, Petra  Schneider, Gisbert  Schneider, JB  Brown. 
-    DOI-URL:      https://doi.org/10.4155/fmc-2016-0197
-            
-
-    Title:        Small Random Forest Models for Effective Chemogenomic Active Learning 
-    Journal:      Journal of Computer Aided Chemistry
-    Contributors: Christin  Rakers, Daniel  Reker, J.B.  Brown. 
-    DOI-URL:      https://doi.org/10.2751/jcac.18.124
-            
-
-    Title:        Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing 
-    Journal:      PLOS Computational Biology
-    Contributors: Hansaim  Lim, Aleksandar  Poleksic, Yuan  Yao, Hanghang  Tong, Di  He, Luke  Zhuang, Patrick  Meng, Lei  Xie, . 
-    DOI-URL:      https://doi.org/10.1371/journal.pcbi.1005135
-            
-Article title:    Matched Molecular Series: Measuring SAR Similarity
-Publication date: May 1, 2017
-DOI-URL:          https://doi.org/10.1021/acs.jcim.6b00709
-
-Subjects:
-Substituents, Mathematical methods, Structure activity relationship, Biological databases
-
-Contributors:
-Emanuel S. R. Ehmki, Christian Kramer
-
-This publication is cited by the following 5 publications:
-
-
-    Title:        Matched Molecular Series Analysis for ADME Property Prediction 
-    Journal:      Journal of Chemical Information and Modeling
-    Contributors: Mahendra Awale, Sereina Riniker, Christian Kramer. 
-    DOI-URL:      https://doi.org/10.1021/acs.jcim.0c00269
-            
-
-    Title:        Approaches using AI in medicinal chemistry 
-    Journal:      
-    Contributors: Christian  Tyrchan, Eva  Nittinger, Dea  Gogishvili, Atanas  Patronov, Thierry  Kogej. 
-    DOI-URL:      https://doi.org/10.1016/B978-0-12-822249-2.00002-5
-            
-
-    Title:        Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods 
-    Journal:      Current Pharmaceutical Design
-    Contributors: Xiaoyu  Ding, Chen  Cui, Dingyan  Wang, Jihui  Zhao, Mingyue  Zheng, Xiaomin  Luo, Hualiang  Jiang, Kaixian  Chen. 
-    DOI-URL:      https://doi.org/10.2174/1381612826666200427111309
-            
-
-    Title:        BRADSHAW: a system for automated molecular design 
-    Journal:      Journal of Computer-Aided Molecular Design
-    Contributors: Darren V. S.  Green, Stephen  Pickett, Chris  Luscombe, Stefan  Senger, David  Marcus, Jamel  Meslamani, David  Brett, Adam  Powell, Jonathan  Masson. 
-    DOI-URL:      https://doi.org/10.1007/s10822-019-00234-8
-            
-
-    Title:        The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction 
-    Journal:      MedChemComm
-    Contributors: Christopher E.  Keefer, George  Chang. 
-    DOI-URL:      https://doi.org/10.1039/C7MD00465F
-