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Yao L, Evans JA, Rzhetsky A.
\newblock Novel opportunities for computational biology and sociology in drug
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\newblock Trends in Biotechnology. 2010 Apr;28(4):161--170.
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Capuzzi SJ, Thornton TE, Liu K, Baker N, Lam WI, O’Banion CP, et~al.
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\newblock Journal of Chemical Information and Modeling. 2018
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Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O, Mathias SL, et~al.
\newblock Associating {Drugs}, {Targets} and {Clinical} {Outcomes} into an
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\newblock Molecular Informatics. 2011 Mar;30(2-3):100--111.
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\bibitem{dura_mining_2014}
Dura E, Muresan S, Engkvist O, Blomberg N, Chen H.
\newblock Mining {Molecular} {Pharmacological} {Effects} from {Biomedical}
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\newblock Molecular Informatics. 2014 May;33(5):332--342.
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\bibitem{piir_best_2018}
Piir G, Kahn I, García-Sosa AT, Sild S, Ahte P, Maran U.
\newblock Best {Practices} for {QSAR} {Model} {Reporting}: {Physical} and
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  {Health}, and {Toxicokinetics} {Endpoints}.
\newblock Environmental Health Perspectives. 2018 Dec;126(12):126001.
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\bibitem{tarasova_data_2019}
Tarasova OA, Biziukova NY, Filimonov DA, Poroikov VV, Nicklaus MC.
\newblock Data {Mining} {Approach} for {Extraction} of {Useful} {Information}
  {About} {Biologically} {Active} {Compounds} from {Publications}.
\newblock Journal of Chemical Information and Modeling. 2019
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\bibitem{krallinger_information_2017}
Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A.
\newblock Information {Retrieval} and {Text} {Mining} {Technologies} for
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\newblock Chemical Reviews. 2017 Jun;117(12):7673--7761.
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\bibitem{corbett_chemlistem_2018}
Corbett P, Boyle J.
\newblock Chemlistem: chemical named entity recognition using recurrent neural
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\newblock Journal of Cheminformatics. 2018 Dec;10(1):59.
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\bibitem{akhondi_recognition_2015}
Akhondi SA, Hettne KM, van~der Horst E, van Mulligen EM, Kors JA.
\newblock Recognition of chemical entities: combining dictionary-based and
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\newblock Journal of Cheminformatics. 2015 Dec;7(S1):S10.
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\bibitem{eltyeb_chemical_2014}
Eltyeb S, Salim N.
\newblock Chemical named entities recognition: a review on approaches and
  applications.
\newblock Journal of Cheminformatics. 2014 Dec;6(1):17.
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\bibitem{zhang_chemical_2016}
Zhang Y, Xu J, Chen H, Wang J, Wu Y, Prakasam M, et~al.
\newblock Chemical named entity recognition in patents by domain knowledge and
  unsupervised feature learning.
\newblock Database. 2016;2016:baw049.
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\bibitem{liu_entity_2017}
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, et~al.
\newblock Entity recognition from clinical texts via recurrent neural network.
\newblock BMC Medical Informatics and Decision Making. 2017 Jul;17(S2):67.
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\bibitem{colon-ruiz_comparing_2020}
Colón-Ruiz C, Segura-Bedmar I.
\newblock Comparing deep learning architectures for sentiment analysis on drug
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\newblock Journal of Biomedical Informatics. 2020 Oct;110:103539.
\newblock Available from:
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\bibitem{hemati_lstmvoter_2019}
Hemati W, Mehler A.
\newblock {LSTMVoter}: chemical named entity recognition using a conglomerate
  of sequence labeling tools.
\newblock Journal of Cheminformatics. 2019 Dec;11(1):3.
\newblock Available from:
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\bibitem{vazquez_text_2011}
Vazquez M, Krallinger M, Leitner F, Valencia A.
\newblock Text {Mining} for {Drugs} and {Chemical} {Compounds}: {Methods},
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\newblock Molecular Informatics. 2011 Jun;30(6-7):506--519.
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\bibitem{biziukova_automated_2020}
Biziukova N, Tarasova O, Ivanov S, Poroikov V.
\newblock Automated {Extraction} of {Information} {From} {Texts} of
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  {Strategies}.
\newblock Frontiers in Genetics. 2020 Dec;11:618862.
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\bibitem{krallinger_chemdner_2015}
Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A.
\newblock {CHEMDNER}: {The} drugs and chemical names extraction challenge.
\newblock Journal of Cheminformatics. 2015 Dec;7(S1):S1.
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\bibitem{Wu-Y-C-2006}
Y~C~Wu YSLSJY T~K~Fan.
\newblock Extracting Named Entities Using Support Vector Machines.
\newblock In: Hunninghake GW, Gadek JE, editors. Knowledge Discovery in Life
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\bibitem{tarasova_computational_2018}
Tarasova O, Biziukova N, Filimonov D, Poroikov V.
\newblock A {Computational} {Approach} for the {Prediction} of {HIV}
  {Resistance} {Based} on {Amino} {Acid} and {Nucleotide} {Descriptors}.
\newblock Molecules. 2018 Oct;23(11):2751.
\newblock Available from: \url{http://www.mdpi.com/1420-3049/23/11/2751}.

\bibitem{li_application_2021}
Li Y, Yin C.
\newblock Application of {Dual}-{Channel} {Convolutional} {Neural} {Network}
  {Algorithm} in {Semantic} {Feature} {Analysis} of {English} {Text} {Big}
  {Data}.
\newblock Computational Intelligence and Neuroscience. 2021 Nov;2021:1--15.
\newblock Available from:
  \url{https://www.hindawi.com/journals/cin/2021/7085412/}.

\bibitem{cho_combinatorial_2020}
Cho M, Ha J, Park C, Park S.
\newblock Combinatorial feature embedding based on {CNN} and {LSTM} for
  biomedical named entity recognition.
\newblock Journal of Biomedical Informatics. 2020 Mar;103:103381.
\newblock Available from:
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\bibitem{chen_named_2019}
Chen Y, Zhou C, Li T, Wu H, Zhao X, Ye K, et~al.
\newblock Named entity recognition from {Chinese} adverse drug event reports
  with lexical feature based {BiLSTM}-{CRF} and tri-training.
\newblock Journal of Biomedical Informatics. 2019 Aug;96:103252.
\newblock Available from:
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\bibitem{gajendran_character_2020}
Gajendran S, D M, Sugumaran V.
\newblock Character level and word level embedding with bidirectional {LSTM}
  – {Dynamic} recurrent neural network for biomedical named entity
  recognition from literature.
\newblock Journal of Biomedical Informatics. 2020 Dec;112:103609.
\newblock Available from:
  \url{https://linkinghub.elsevier.com/retrieve/pii/S1532046420302367}.

\bibitem{bundschus_extraction_2008}
Bundschus M, Dejori M, Stetter M, Tresp V, Kriegel HP.
\newblock Extraction of semantic biomedical relations from text using
  conditional random fields.
\newblock BMC Bioinformatics. 2008 Dec;9(1):207.
\newblock Available from:
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\bibitem{shu_sensitivity_2019}
Shu H, Zhu H.
\newblock Sensitivity {Analysis} of {Deep} {Neural} {Networks}.
\newblock Proceedings of the AAAI Conference on Artificial Intelligence. 2019
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\newblock Available from:
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\bibitem{li_analyzing_2021}
Li Z, Kamnitsas K, Glocker B.
\newblock Analyzing {Overfitting} {Under} {Class} {Imbalance} in {Neural}
  {Networks} for {Image} {Segmentation}.
\newblock IEEE Transactions on Medical Imaging. 2021 Mar;40(3):1065--1077.
\newblock Available from: \url{https://ieeexplore.ieee.org/document/9302891/}.

\bibitem{baskin_renaissance_2016}
Baskin II, Winkler D, Tetko IV.
\newblock A renaissance of neural networks in drug discovery.
\newblock Expert Opinion on Drug Discovery. 2016 Aug;11(8):785--795.
\newblock Available from:
  \url{http://www.tandfonline.com/doi/full/10.1080/17460441.2016.1201262}.

\bibitem{tarasova_pass-based_2017}
Tarasova O, Filimonov D, Poroikov V.
\newblock {PASS}-based approach to predict {HIV}-1 reverse transcriptase
  resistance.
\newblock Journal of Bioinformatics and Computational Biology. 2017
  Apr;15(02):1650040.
\newblock Available from:
  \url{https://www.worldscientific.com/doi/abs/10.1142/S0219720016500402}.

\bibitem{tarasova_hiv_2018}
Tarasova O, Poroikov V.
\newblock {HIV} {Resistance} {Prediction} to {Reverse} {Transcriptase}
  {Inhibitors}: {Focus} on {Open} {Data}.
\newblock Molecules. 2018 Apr;23(4):956.
\newblock Available from: \url{http://www.mdpi.com/1420-3049/23/4/956}.

\bibitem{pubmed}
National Library of Medicine;.
\newblock Available from: \url{https://pubmed.ncbi.nlm.nih.gov/}.

\bibitem{NLTK}
National language toolkit;.
\newblock Available from: \url{https://www.nltk.org/api/nltk.tokenize.html}.

\bibitem{Filimonov-book}
Filimonov DA, Poroikov VV.
\newblock Probabilistic approach in activity prediction.
\newblock In: Varnek A, Tropsha A, editors. Chemoinformatics Approaches to
  Virtual Screening. Cambridge (UK): RSC Publishing; 2008. p. 182--216.

\bibitem{uniprot}
The Universal Protein Resource (UniProt);.
\newblock Available from: \url{www.uniprot.org}.

\bibitem{pubchem}
PubChem, an open chemistry database at the National Institutes of Health
  (NIH);.
\newblock Available from: \url{https://pubchem.ncbi.nlm.nih.gov/}.

\bibitem{chembl}
ChEMBL, a manually curated database of bioactive molecules;.
\newblock Available from: \url{https://pubchem.ncbi.nlm.nih.gov/}.

\bibitem{jama-clin}
W~H~Self MWSea.
\newblock Effect of Hydroxychloroquine on Clinical Status at 14 Days in
  Hospitalized Patients With COVID19: A Randomized Clinical Trial.
\newblock JAMA; 2020.

\bibitem{abian_structural_2020}
Abian O, Ortega-Alarcon D, Jimenez-Alesanco A, Ceballos-Laita L, Vega S,
  Reyburn HT, et~al.
\newblock Structural stability of {SARS}-{CoV}-2 {3CLpro} and identification of
  quercetin as an inhibitor by experimental screening.
\newblock International Journal of Biological Macromolecules. 2020
  Dec;164:1693--1703.
\newblock Available from:
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\bibitem{al-karmalawy_naturally_2021}
Al-Karmalawy AA, Farid MM, Mostafa A, Ragheb AY, H~Mahmoud S, Shehata M, et~al.
\newblock Naturally {Available} {Flavonoid} {Aglycones} as {Potential}
  {Antiviral} {Drug} {Candidates} against {SARS}-{CoV}-2.
\newblock Molecules. 2021 Oct;26(21):6559.
\newblock Available from: \url{https://www.mdpi.com/1420-3049/26/21/6559}.

\bibitem{xiao_effect_2021}
Xiao T, Wei Y, Cui M, Li X, Ruan H, Zhang L, et~al.
\newblock Effect of dihydromyricetin on {SARS}-{CoV}-2 viral replication and
  pulmonary inflammation and fibrosis.
\newblock Phytomedicine. 2021 Oct;91:153704.
\newblock Available from:
  \url{https://linkinghub.elsevier.com/retrieve/pii/S0944711321002476}.

\bibitem{tarasova_extraction_2021}
Tarasova OA, Biziukova NY, Rudik AV, Dmitriev AV, Filimonov DA, Poroikov VV.
\newblock Extraction of {Data} on {Parent} {Compounds} and {Their}
  {Metabolites} from {Texts} of {Scientific} {Abstracts}.
\newblock Journal of Chemical Information and Modeling. 2021
  Apr;61(4):1683--1690.
\newblock Available from:
  \url{https://pubs.acs.org/doi/10.1021/acs.jcim.0c01054}.

\bibitem{kar_how_2013}
Kar S, Roy K.
\newblock How far can virtual screening take us in drug discovery?
\newblock Expert Opinion on Drug Discovery. 2013 Mar;8(3):245--261.
\newblock Available from:
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\end{thebibliography}
