Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

Advanced Review

Alfonso T. García‐Sosa, Paul A. Nguewa, José Peña‐Guerrero

Published Online: Jan 05 2021

DOI: 10.1002/wcms.1513

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Abstract Machine learning (ML) is becoming capable of transforming biomolecular interaction description and calculation, promising an impact on molecular and drug design, chemical biology, toxicology, among others. The first improvements can be seen from biomolecule structure prediction to chemical synthesis, molecular generation, mechanism of action elucidation, inverse design, polypharmacology, organ or issue targeting of compounds, property and multiobjective optimization. Chemical design proposals from an algorithm may be inventive and feasible. Challenges remain, with the availability, diversity, and quality of data being critical for developing useful ML models; marginal improvement seen in some cases, as well as in the interpretability, validation, and reuse of models. The ultimate aim of ML should be to facilitate options for the scientist to propose and undertake ideas and for these to proceed faster. Applications are ripe for transformative results in understudied, neglected, and rare diseases, where new data and therapies are strongly required. Progress and outlook on these themes are provided in this study. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures

A molecular design chemical Turing test, where a machine learning (ML), artificial intelligence (AI), or data science technique (a) proposes a chemical compound that to a chemist's (c) perception and judgment is indistinguishable from another chemist's (b) proposed structure

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References

Schneider, P, Walters, WP, Plowright, AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov Nat Res. 2020;19:353–364.
Sandfort, F, Strieth‐Kalthoff, F, Kühnemund, M, Beecks, C, Glorius, F. A structure‐based platform for predicting chemical reactivity. Chem. 2020;6(6):1379–1390.
Mayr, A, Klambauer, G, Unterthiner, T, et al. Large‐scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci. 2018;9(24):5441–5451.
Mason, DJ, Eastman, RT, Lewis, RPI, Stott, IP, Guha, R, Bender, A. Using machine learning to predict synergistic antimalarial compound combinations with novel structures. Front Pharmacol. 2018;9(OCT):1096.
Schmidt, J, Marques, MRG, Botti, S, Marques, MAL. Recent advances and applications of machine learning in solid‐state materials science. npj Comput Mater. 2019;5:1–36.
Choo, K, Mezzacapo, A, Carleo, G. Fermionic neural‐network states for ab‐initio electronic structure. Nat Commun. 2020;11(1):2368.
Cole, DJ, Mones, L, Csányi, G. A machine learning based intramolecular potential for a flexible organic molecule. Faraday Discuss. 2020;224:247–264.
Muratov, EN, Bajorath, J, Sheridan, RP, et al. QSAR without borders. Chem Soc Rev. 2020;49(11):3525–3564.
Bragato, M, von Rudorff, GF, von Lilienfeld, OA. Data enhanced Hammett‐equation: reaction barriers in chemical space. Chem Sci. 2020;11(43):11859–11868.
Turing test | Definition %26 Facts | Britannica [Internet]. https://www.britannica.com/technology/Turing-test. Accessed 13 Nov 2020.
Krenn, M, Hase, F, Nigam, A, Friederich, P, Aspuru‐Guzik, A. Self‐referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach Learn Sci Technol. 2020;1(4):045024.
Friedman, N, Geiger, D, Goldszmidt, M. Bayesian network classifiers. Mach Learn. 1997;29:131–163.
Breiman, L. Random Forests. Machine Learning. 2001;45(1):5–32. http://dx.doi.org/10.1023/a:1010933404324.
Quinlan, J. C4.5. Programs for machine learning. San Mateo, CA: Morgan Kaufman; 2014.
Cortes, C, Vapnik, V. Support‐vector networks. Machine Learning. 1995;203:273–297. http://dx.doi.org/10.1007/bf00994018.
Ragoza, M, Hochuli, J, Idrobo, E, Sunseri, J, Koes, DR. Protein–ligand scoring with convolutional neural networks. J Chem Inf Model. 2017;57(4):942–957.
Anastasia Kyrykovych, L. Deep neural networks [Internet]. https://www.kdnuggets.com/2020/02/deep-neural-networks.html. Accessed 13 Nov 2020.
Elton, DC, Boukouvalas, Z, Fuge, MD, Chung, PW. Deep learning for molecular design—a review of the state of the art. Mol Syst Des Eng. 2019;4(4):828–849.
Zhou, Z‐H. Ensemble methods: foundations and algorithms. Boca Raton: Chapman %26 Hall/CRC, 2012;p. 236.
XGBoost Documentation—xgboost 1.3.0‐SNAPSHOT documentation [Internet]. https://xgboost.readthedocs.io/en/latest/. Accessed 13 Nov 2020.
Liu, H, Wang, L, Lv, M, et al. AlzPlatform: an Alzheimer`s disease domain‐specific chemogenomics knowledgebase for polypharmacology and target identification research. J Chem Inf Model. 2014;54(4):1050–1060.
Difference between PCA VS t‐SNE—GeeksforGeeks [Internet]. https://www.geeksforgeeks.org/difference-between-pca-vs-t-sne/. Accessed 13 Nov 2020.
Yang, J, Shen, C, Huang, N. Predicting or pretending: artificial intelligence for protein–ligand interactions lack of sufficiently large and unbiased datasets. Front Pharmacol. 2020;11:69.
García‐Sosa, AT. Benford`s law in medicinal chemistry: implications for drug design. Future Med Chem. 2019;11(17):2247–2253. https://doi.org/10.4155/fmc-2019-0006.
Hanson‐Heine, MWD, Ashmore, AP. Computational chemistry experiments performed directly on a blockchain virtual computer. Chem Sci. 2020;11(18):4644–4647.
Reker, D. Practical considerations for active machine learning in drug discovery. Drug Discovery Today: Technologies. 2020. http://dx.doi.org/10.1016/j.ddtec.2020.06.001.
Finn, C, Abbeel, P, Levine, S. Model‐agnostic meta‐learning for fast adaptation of deep networks. In: 34th International Conference on Machine Learning, ICML 2017; 2017. p. 1856–1868.
Pan, SJ, Yang, QA. Survey on transfer learning. IEEE Trans Knowl Data Eng. 2009;22(10):1345–1359.
Unterthiner, T, Mayr, A, Unter Klambauer, G, et al. Deep learning as an opportunity in virtual screening. Proc Deep Learn Work NIPS. 2014;27:1–9.
Snell, J, Swersky, K, Zemel, R. Prototypical networks for few‐shot learning. Adv Neural Inf Process Syst. 2017;30:4077–4087.
Segler, MHS, Kogej, T, Tyrchan, C, Waller, MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci. 2018;4(1):120–131.
Gómez‐Bombarelli, R, Wei, JN, Duvenaud, D, et al. Automatic chemical design using a data‐driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268–276.
Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell. 2019;267:1–38.
Kim, B, Khanna, R, Koyejo, O. Examples are not enough, learn to criticize! Criticism for interpretability. Adv Neural Inf Process Syst. 2016;29:2280–2288.
Chapter 2. Interpretability | Interpretable machine learning [Internet]. https://christophm.github.io/interpretable-ml-book/interpretability.html. Accessed 13 Nov 2020.
Freeze, JG, Kelly, HR, Batista, VS. Search for catalysts by inverse design: Artificial intelligence, mountain climbers, and alchemists. Chem Rev. 2019;119:6595–6612.
Froemming, NS, Henkelman, G. Optimizing core–shell nanoparticle catalysts with a genetic algorithm. J Chem Phys. 2009;131(23):234103.
Coley, CW, Eyke, NS, Jensen, KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angewandte Chemie International Edition. 2020;59(51):22858–22893.
Coley, CW, Eyke, NS, Jensen, KF. Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angewandte Chemie International Edition. 2020;59(52):23414–23436.
Chen, H, Engkvist, O. Has drug design augmented by artificial intelligence become a reality? Trends Pharmacol Sci. 2019;40:806–809.
Friederich, P, Dos Passos Gomes, G, De Bin, R, Aspuru‐Guzik, A, Balcells, D. Machine learning dihydrogen activation in the chemical space surrounding Vaska`s complex. Chem Sci. 2020;11(18):4584–4601.
MacLeod, BP, Parlane, FGL, Morrissey, TD, et al. Self‐driving laboratory for accelerated discovery of thin‐film materials. Sci Adv. 2020;6(20):eaaz8867.
Burger, B, Maffettone, PM, Gusev, VV, et al. A mobile robotic chemist. Nature. 2020;583(7815):237–241.
Smith, JS, Nebgen, BT, Zubatyuk, R, et al. Approaching coupled cluster accuracy with a general‐purpose neural network potential through transfer learning. Nat Commun. 2019;10(1):2903.
Lahey, SLJ, Rowley, CN. Simulating protein–ligand binding with neural network potentials. Chem Sci. 2020;11(9):2362–2368.
Faber, FA, Hutchison, L, Huang, B, et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theory Comput. 2017;13(11):5255–5264.
Parks, CD, Gaieb, Z, Chiu, M, et al. D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des. 2020;34(2):99–119.
Senior, AW, Evans, R, Jumper, J, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706–710.
Glavatskikh, M, Leguy, J, Hunault, G, Cauchy, T, Da Mota, B. Dataset`s chemical diversity limits the generalizability of machine learning predictions. J Chem. 2019;11(1):1–15.
Plante, A, Shore, DM, Morra, G, Khelashvili, G, Weinstein, HA. Machine learning approach for the discovery of ligand‐specific functional mechanisms of GPCRs. Molecules. 2019;24(11):2097
Wheatley, M, Wootten, D, Conner, MT, et al. Lifting the lid on GPCRs: The role of extracellular loops. Br J Pharmacol. 2012;165:1688–1703.
Katritch, V, Cherezov, V, Stevens, RC. Structure‐function of the G protein‐coupled receptor superfamily. Annu Rev Pharmacol Toxicol Annu Rev. 2013;53:531–556.
Wang, Y, Lamim Ribeiro, JM, Tiwary, P. Machine learning approaches for analyzing and enhancing molecular dynamics simulations. Curr Opin Struct Biol. 2020;61:139–145.
Faber, FA, Hutchison, L, Huang, B, et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theory Comput. 2017;13(11):5255–5264.
Le, N‐Q‐K, Ho, Q‐T, Ou, Y‐Y. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins. J Comput Chem. 2017;38(23):2000–2006.
Do, DT, Quynh, T, Le, T, Quoc, N, Le, K. Using deep neural networks and biological subwords to detect protein S‐sulfenylation sites. Brief Bioinform. 2020;2020:1–11.
Kuenzi, BM, Park, J, Fong, SH, et al. Predicting drug response and synergy using a deep learning model of human Cancer cells. Cancer Cell. 2020;38(5):672–684.e6.
Vamathevan, J, Clark, D, Czodrowski, P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463–477.
Myszczynska, MA, Ojamies, PN, Lacoste, AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol Nat Res. 2020;16:440–456.
Ericksen, SS, Wu, H, Zhang, H, et al. Machine learning consensus scoring improves performance across targets in structure‐based virtual screening. J Chem Inf Model. 2017;57(7):1579–1590.
Mysinger, MM, Carchia, M, Irwin, JJ, Shoichet, BK. Directory of useful decoys, enhanced (DUD‐E): better ligands and decoys for better benchmarking. J Med Chem. 2012;55(14):6582–6594.
Del Rosario, Z, Rupp, M, Kim, Y, Antono, E, Ling, J. Assessing the frontier: active learning, model accuracy, and multi‐objective candidate discovery and optimization. J Chem Phys. 2020;153(2):024112.
Amabilino, S, Pogány, P, Pickett, SD, Green, DVS. Guidelines for recurrent neural network transfer learning‐based molecular generation of focused libraries. J Chem Inf Model. 2020;60:5699–5713.
Yosipof, A, Guedes, RC, García‐Sosa, AT. Data mining and machine learning models for predicting drug likeness and their disease or organ category. Frontiers in Chemistry. 2018;6:162. http://dx.doi.org/10.3389/fchem.2018.00162.
Esposito, C, Wang, S, Lange, UEW, Oellien, F, Riniker, S. Combining machine learning and molecular dynamics to predict P‐glycoprotein substrates. J Chem Inf Model. 2020;60:4730–4749.
Chen, H, Engkvist, O, Wang, Y, Olivecrona, M, Blaschke, T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250.
Zhang, J, Mucs, D, Norinder, U, Svensson, F. LightGBM: an effective and scalable algorithm for prediction of chemical toxicity‐application to the Tox21 and mutagenicity aata sets. J Chem Inf Model. 2019;59(10):4150–4158.
Struble, TJ, Alvarez, JC, Brown, SP, et al. Current and future roles of artificial intelligence in medicinal chemistry synthesis. J Med Chem. 2020;63(16):8667–8682.
Zhavoronkov, A, Ivanenkov, YA, Aliper, A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038–1040.
Walters, WP, Murcko, M. Assessing the impact of generative AI on medicinal chemistry. Nat Biotechnol. 2020;38:143–145.
Wakefield, J. Artificial intelligence‐created medicine to be used on humans for first time. BBC News [Internet]; 2020. https://www.bbc.com/news/technology-51315462. Accessed 09 Sep 2020.
Smith, J. Exscientia`s first AI‐designed drug enters phase I to treat OCD [Internet]; 2020. https://www.labiotech.eu/ai/exscientia-ocd-ai-sumitomo/. Accessed 09 Sep 2020.
Tucs, A, Tran, DP, Yumoto, A, Ito, Y, Uzawa, T, Tsuda, K. Generating ampicillin‐level antimicrobial peptides with activity‐aware generative adversarial networks. ACS Omega. 2020;5:22847–22851.
Blaschke, T, Olivecrona, M, Engkvist, O, Bajorath, J, Chen, H. Application of generative autoencoder in de novo molecular design. Mol Inform. 2018;37(1):1700123
Polishchuk, P. CReM: chemically reasonable mutations framework for structure generation. J Chem. 2020;12(28):1–18.
Ståhl, N, Falkman, G, Karlsson, A, Mathiason, G, Boström, J. Deep reinforcement learning for multiparameter optimization in de novo drug design. J Chem Inf Model. 2019;59(7):3166–3176.
Matsuzaka, Y, Uesawa, Y. DeepSnap‐deep learning approach predicts progesterone receptor antagonist activity with high performance. Front Bioeng Biotechnol. 2019;7:485.
Yang, X, Wang, Y, Byrne, R, Schneider, G, Yang, S. Concepts of artificial intelligence for computer‐assisted drug discovery. Chem Rev. 2019;119:10520–10594.
Desai, B, Dixon, K, Farrant, E, et al. Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. J Med Chem. 2013;56(7):3033–3047.
Reker, D, Schneider, P, Schneider, G. Multi‐objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors. Chem Sci. 2016;7(6):3919–3927.
Fujiwara, Y, Yamashita, Y, Osoda, T, et al. Virtual screening system for finding structurally diverse hits by active learning. J Chem Inf Model. 2008;48(4):930–940.
Duros, V, Grizou, J, Xuan, W, et al. Human versus robots in the discovery and crystallization of gigantic Polyoxometalates. Angew Chem Int Ed. 2017;56(36):10815–10820.
Diamond Light Source. Main protease structure and XChem fragment screen. Diamond Light Source [Internet]; 2020. https://www.diamond.ac.uk/covid-19/for-scientists/Main-protease-structure-and-XChem.html. Accessed 09 Sep 2020.
Jasial, S, Gilberg, E, Blaschke, T, Bajorath, J. Machine learning distinguishes with high accuracy between pan‐assay interference compounds that are promiscuous or represent dark chemical matter. J Med Chem. 2018;61(22):10255–10264.
Cruz‐Monteagudo, M, Medina‐Franco, JL, Pérez‐Castillo, Y, Nicolotti, O, Cordeiro, MNDS, Borges, F. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discov Today. 2014;19:1069–1080.
Rodríguez‐Pérez, R, Miljković, F, Bajorath, J. Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J Chem. 2020;12:36.
García‐Sosa, AT, Sild, S, Maran, U. Docking and virtual screening using distributed grid technology. QSAR Comb Sci. 2009;28(8):815–821. https://doi.org/10.1002/qsar.200810174.
Lyu, J, Wang, S, Balius, TE, et al. Ultra‐large library docking for discovering new chemotypes. Nature. 2019;566(7743):224–229.
Viira, B, Selyutina, A, García‐Sosa, AT, et al. Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s‐triazine derivatives as HIV‐1 non‐nucleoside reverse transcriptase inhibitors. Bioorganic Med Chem. 2016;24(11):2519–2529. https://doi.org/10.1016/j.bmc.2016.04.018.
Glisic, S, Sencanski, M, Perovic, V, Stevanovic, S, García‐Sosa, AT. Arginase flavonoid anti‐leishmanial in silico inhibitors flagged against anti‐targets. Molecules. 2016;21(5):589. http://dx.doi.org/10.3390/molecules21050589.
Stevanovic, S, Sencanski, M, Danel, M, et al. Synthesis, in silico, and in vitro evaluation of anti‐Leishmanial activity of oxadiazoles and indolizine containing compounds flagged against anti‐targets. Molecules. 2019;24(7):1282. https://doi.org/10.3390/molecules24071282.
García‐Sosa, AT, Hetényi, C, Maran, U. Drug efficiency indices for improvement of molecular docking scoring functions. J Comput Chem. 2010;31(1):174–184. https://doi.org/10.1002/jcc.21306.
García‐Sosa, AT, Mancera, RL, Dean, PM. WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein–ligand complexes. J Mol Model. 2003;9(3):172–182. http://dx.doi.org/10.1007/s00894-003-0129-x.
Jiménez‐Luna, J, Cuzzolin, A, Bolcato, G, Sturlese, M, Moro, S. A deep‐learning approach toward rational molecular docking protocol selection. Molecules. 2020;25(11):2487.
Adeshina, YO, Deeds, EJ, Karanicolas, J. Machine learning classification can reduce false positives in structure‐based virtual screening. Proceedings of the National Academy of Sciences. 2020;11731:18477–18488.
Chen, L, Cruz, A, Ramsey, S, et al. Hidden bias in the DUD‐E dataset leads to misleading performance of deep learning in structure‐based virtual screening. PLoS One. 2019;14(8):e0220113.
Moret, M, Friedrich, L, Grisoni, F, Merk, D, Schneider, G. Generative molecular design in low data regimes. Nat Mach Intell. 2020;2(3):171–180.
Scantlebury, J, Brown, N, Von, DF. BioRxiv CD‐, 2020 U. Dataset augmentation allows Deep learning‐based virtual screening to better generalize to unseen target classes, and highlight important binding interactions. J Chem Inf Model. 2020;60(8):3722–3730.
Vanden Eynde, JJ, Mangoni, AA, Rautio, J, et al. Breakthroughs in medicinal chemistry: new targets and mechanisms, new drugs, new hopes‐6. Molecules. 2020;25(1):119
Whitehouse, AJ, Libardo, MDJ, Kasbekar, M, et al. Targeting of fumarate hydratase from Mycobacterium tuberculosis using allosteric inhibitors with a dimeric‐binding mode. J Med Chem. 2019;62(23):10586–10604.
Alcaro, S, Bolognesi, ML, García‐Sosa, AT, Rapposelli, S. Editorial: Multi‐target‐directed ligands (MTDL) as challenging research tools in drug discovery: from design to pharmacological evaluation. Front Chem. 2019;7:71. https://doi.org/10.3389/fchem.2019.00071.
Garcia‐Sosa, AT. Designing ligands for leishmania, plasmodium, and aspergillus N‐myristoyl transferase with specificity and anti‐target‐safe virtual libraries. Curr Comput Aided Drug Des. 2018;14(2):131–141.
Mizuno, S, Iijima, R, Ogishima, S, et al. AlzPathway: a comprehensive map of signaling pathways of Alzheimer`s disease. BMC Syst Biol. 2012;6:52.
Michigan state university investigator receives $2.1m to study existing treatments for select rare diseases [Internet]. TrialSiteNews; 2019. https://www.trialsitenews.com/michigan-state-university-investigator-receives-2-1m-to-study-existing-treatments-for-select-rare-diseases/. Accessed 09 Sep 2020.
Nielsen, AN, Gratton, C, Church, JA, et al. Atypical functional connectivity in Tourette syndrome differs between children and adults. Biol Psychiatry. 2020;87(2):164–173.
Gradus, JL, Rosellini, AJ, Horváth‐Puhó, E, et al. Prediction of sex‐specific suicide risk using machine learning and single‐payer health care registry data from Denmark. JAMA Psychiat. 2020;77(1):25–34.
Ekins, S, Perlstein, EO. Doing it all—how families are reshaping rare disease research. Pharmaceutical research. Volume 35. New York: . Springer, 2018.
Aung, MT, Yu, Y, Ferguson, KK, et al. Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers. Sci Rep. 2019;9(1):17049.
Le, NQK. Fertility‐GRU: Identifying fertility‐related proteins by incorporating deep‐gated recurrent units and original position‐specific scoring matrix profiles. J Proteome Res. 2019;18(9):3503–3511.
Word Health Organization. Working to overcome the global impact of neglected tropical diseases. First WHO report on neglected tropical diseases; 2010.
Vardell, E. Global health observatory data repository. Med Ref Serv Q. 2020;39(1):67–74.
World Health Organization. Chagas disease: Fact sheet [Internet]. Vol. 304, Geneve: Technical Report Series; 2019. p. 1–4. https://www.who.int/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis). Accessed 29 Jul 2020.
World Health Organization. Dracunculiasis (guinea‐worm disease) fact sheet [Internet]. World Health Organization; 2020. https://www.who.int/news-room/fact-sheets/detail/dracunculiasis-(guinea-worm-disease). Accessed 29 Jul 2020.
World Health Organization. Echinococcosis fact sheet [Internet]. World Health Organization; 2020. https://www.who.int/news-room/fact-sheets/detail/echinococcosis. Accessed 29 Jul 2020.
World Health Organization. Foodborne trematodiases [Internet]. Fact Sheet; 2016. p. 6–11. https://www.who.int/news-room/fact-sheets/detail/foodborne-trematodiases. Accessed 29 Jul 2020.
World Health Organization. WHO: lymphatic filariasis epidemiology [Internet]. World Health Organization; 2018. http://www.who.int/lymphatic_filariasis/epidemiology/en/. Accessed 29 Jul 2020.
World Health Organization. Schistosomiasis, Fact sheet February 2016 [Internet]. World Health Organization (WHO); 2016. p. 1–5. https://www.who.int/news-room/fact-sheets/detail/schistosomiasis. Accessed 29 Jul 2020.
World Health Organization Media Centre. Soil‐transmitted helminth infections. Fact sheet N°366 [Internet]. Fact Sheet; 2014. https://www.who.int/news-room/fact-sheets/detail/soil-transmitted-helminth-infections. Accessed 29 Jul 2020.
World Health Organization. Yaws: fact sheets. World Heal Organ. 2019.
World Health Organization. Media centre: dengue and severe dengue fact sheet. World Heal Organ. 2016;1–7.
World Health Organization. WHO chikungunya fact sheet [Internet]. World Health Organization Media Centre; 2015. p. 1–2. https://www.who.int/news-room/fact-sheets/detail/chikungunya. Accessed 29 Jul 2020.
van de Sande, WWJ. Global burden of human mycetoma: a systematic review and meta‐analysis. PLoS Negl Trop Dis. 2013;7(11):e2550.
World Health Organization. WHO | Scabies and other ectoparasites [Internet]. World Health Organization; 2020. http://www.who.int/neglected_diseases/diseases/scabies-and-other-ectoparasites/en/. Accessed 29 Jul 2020.
Bernhardt, V, Finkelmeier, F, Verhoff, MA, Amendt, J. Myiasis in humans—a global case report evaluation and literature analysis. Parasitology research. 2019;118:389–397.
World Health Organization. Ebola virus disease: fact sheet No. 103 [Internet]; 2015. http://www.who.int/mediacentre/factsheets/fs103/en/. Accessed 29 Jul 2020. Media Centre. https://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease.
World Health Organization. WHO coronavirus disease (COVID‐19) dashboard [Internet]. WHO; 2020. https://covid19.who.int/. Accessed 29 Jul 2020.
Fitzpatrick, C, Nwankwo, U, Lenk, E, de Vlas, SJ, Bundy, DAP. An investment case for ending neglected tropical diseases. Disease control priorities, 3rd ed. (Vol. 6): Major infectious diseases. Washington: The World Bank, 2017; p. 411–431.
Hotez, PJ, Molyneux, DH, Fenwick, A, et al. Control of neglected tropical diseases. N Engl J Med. 2007;357:1018–1027.
Word Health Organization. Global Health Observatory (GHO) data. Neglected tropical diseases.
DiMasi, JA, Grabowski, HG, Hansen, RW. Innovation in the pharmaceutical industry: New estimates of R%26D costs. J Health Econ. 2016;47:20–33.
Ekins, S, Puhl, AC, Zorn, KM, et al. Exploiting machine learning for end‐to‐end drug discovery and development. Nat Mater. 2019;18:435–441.
Reker, D, Rodrigues, T, Schneider, P, Schneider, G. Identifying the macromolecular targets of de novo‐designed chemical entities through self‐organizing map consensus. Proc Natl Acad Sci U S A. 2014;111(11):4067–4072.
Lampa, S, Alvarsson, J, McShane, SA, Berg, A, Ahlberg, E, Spjuth, O. Predicting off‐target binding profiles with confidence using conformal prediction. Front Pharmacol. 2018;9:1256
Gaulton, A, Bellis, LJ, Bento, AP, et al. ChEMBL: a large‐scale bioactivity database for drug discovery. Nucleic Acids Research. 2012;40D1:D1100–D1107.
Kim, S, Thiessen, PA, Bolton, EE, et al. PubChem substance and compound databases. Nucleic Acids Res. 2015;44(D):D1202–D1213.
Clark, AM, Williams, AJ, Ekins, S. Machines first, humans second: On the importance of algorithmic interpretation of open chemistry data. J Chem. 2015;7:9
DeepChem [Internet]; 2020. https://deepchem.io/docs/index.html. Accessed 31st December 2020.
RDKit. Open‐source cheminformatics and machine learning [Internet]; 2020. https://rdkit.blogspot.com/. Accessed 09 Sep 2020.
Wu, Z, Ramsundar, B, Feinberg, EN, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018;9(2):513–530.
PyTorch. PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. [Internet]; 2020. https://pytorch.org/docs/stable/index.html. Accessed 09 Sep 2020.
KNIME [Internet]. https://www.knime.com/about. Accessed 09 Sep 2020.
Skuta, C, Popr, M, Muller, T, et al. Probes %26 drugs portal: an interactive, open data resource for chemical biology. Nat Methods. 2017;14:759–760.
Kim, S, Thiessen, PA, Bolton, EE, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–D1213.
Papadatos, G, Davies, M, Dedman, N, et al. SureChEMBL: a large‐scale, chemically annotated patent document database. Nucleic Acids Res. 2016;44(D1):D1220–D1228.
Schaduangrat, N, Lampa, S, Simeon, S, Gleeson, MP, Spjuth, O, Nantasenamat, C. Towards reproducible computational drug discovery. J Chem. 2020;12:1–30.
Orphanet [Internet]. https://www.orpha.net/consor/cgi-bin/index.php. Accessed 16 Nov 2020.
Tox21. Overview [Internet]. https://tox21.gov/overview/. Accessed 16 Nov 2020.
EU‐ToxRisk—EU‐ToxRisk—An Integrated European ‘Flagship’ Programme Driving Mechanism‐based Toxicity Testing and Risk Assessment for the 21st century [Internet]. https://www.eu-toxrisk.eu/. Accessed 16 Nov 2020.
The OECD QSAR Toolbox—OECD [Internet]. https://www.oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm. Accessed 16 Nov 2020.
pandas—Python Data Analysis Library [Internet]. https://pandas.pydata.org/. Accessed 16 Nov 2020.
scikit‐learn: machine learning in Python—scikit‐learn 0.23.2 documentation [Internet]. https://scikit-learn.org/stable/. Accessed 16 Nov 2020.
ChEMBL‐NTD—ChEMBL‐NTD [Internet]. https://chembl.gitbook.io/chembl-ntd/. Accessed 16 Nov 2020.
TDR Targets [Internet]. https://tdrtargets.org/. Accessed 16 Nov 2020.
Drug discovery | DNDi [Internet]. https://dndi.org/research-development/drug-discovery/. Accessed 16 Nov 2020.
García‐Sosa, AT, Oja, M, Hetényi, C, Maran, U. DrugLogit: logistic discrimination between drugs and nondrugs including disease‐specificity by assigning probabilities based on molecular properties. J Chem Inf Model. 2012;52(8):2165–2180. https://dx.doi.org/10.1021/ci200587h.
Medicines for Malaria Venture | Developing antimalarials to save lives [Internet]. https://www.mmv.org/. Accessed 16 Nov 2020.
The Pathogen Box | Medicines for Malaria Venture [Internet]. https://www.mmv.org/mmv-open/pathogen-box. Accessed 18 Nov 2020.
The Pandemic Response Box | Medicines for Malaria Venture [Internet]. https://www.mmv.org/mmv-open/pandemic-response-box. Accessed 16 Nov 2020.
About the Malaria Box | Medicines for Malaria Venture [Internet]. https://www.mmv.org/mmv-open/malaria-box/about-malaria-box. Accessed 16 Nov 2020.
TriTrypDB [Internet]. https://tritrypdb.org/tritrypdb/app. Accessed 16 Nov 2020.
VEuPathDB [Internet]. https://veupathdb.org/veupathdb/app/. Accessed 16 Nov 2020.
Home—Gene—NCBI [Internet]. https://www.ncbi.nlm.nih.gov/gene. Accessed 16 Nov 2020.
PANTHER—Gene list analysis [Internet]. http://www.pantherdb.org/. Accessed 16 Nov 2020.
SwissADME [Internet]. http://www.swissadme.ch/index.php. Accessed 16 Nov 2020.
InterPro [Internet]. http://www.ebi.ac.uk/interpro/. Accessed 16 Nov 2020.
Pfam: Home page [Internet]. http://pfam.xfam.org/. Accessed 16 Nov 2020.
SMART: Main page [Internet]. http://smart.embl-heidelberg.de/. Accessed 16 Nov 2020.
SUPERFAMILY database of structural and functional protein annotations for all completely sequenced organisms [Internet]. http://supfam.org/SUPERFAMILY/index.html. Accessed 16 Nov 2020.
SSGCID | SSGCID [Internet]. https://www.ssgcid.org/. Accessed 16 Nov 2020.
KEGG: Kyoto Encyclopedia of Genes and Genomes [Internet]. https://www.genome.jp/kegg/. Accessed 16 Nov 2020.
UniProt [Internet]. https://www.uniprot.org/. Accessed 16 Nov 2020.
GeneDB—Home [Internet]. https://www.genedb.org/. Accessed 16 Nov 2020.
RCSB PDB: Homepage [Internet]. https://www.rcsb.org/. Accessed 16 Nov 2020.
ZINC [Internet]. http://zinc20.docking.org/. Accessed 16 Nov 2020.
Irwin, JJ, Tang, KG, Young, J, et al. ZINC20—a free ultralarge‐scale chemical database for ligand discovery. J Chem Inf Model. 2020;60:6065–6073.
Ekins, S, de Siqueira‐Neto, JL, Mccall, LI, et al. Machine learning models and pathway genome data base for trypanosoma cruzi drug discovery. PLoS Negl Trop Dis. 2015;9(6):e0003878.
Collaborative Drug Discovery. Collaborative drug discovery public; 2015. https://www.collaborativedrug.com/public-access/. Accessed 28 Dec 2020.
World Health Organization. Reports of the World Health Organization 2011; 2011.
Croft, SL, Coombs, GH. Leishmaniasis—current chemotherapy and recent advances in the search for novel drugs. Trends Parasitol. 2003;19:502–508.
Jamal, S, Scaria, V. Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana. BMC Bioinformatics. 2013;14(1):329.
Wang, Y, Xiao, J, Suzek, TO, Zhang, J, Wang, J, Bryant, SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37:623–633.
2C4C Model Repository—Vinod Scaria MBBS, PhD [Internet]. http://vinodscaria.rnabiology.org/2C4C/models. Accessed 11 Sep 2020.
Santa Maria, JP, Park, Y, Yang, L, et al. Linking high‐throughput screens to identify MoAs and novel inhibitors of Mycobacterium tuberculosis dihydrofolate reductase. ACS Chem Biol. 2017;12(9):2448–2456.
Glick, M, Jenkins, JL, Nettles, JH, Hitchings, H, Davies, JW. Enrichment of high‐throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian‐modified naive Bayesian classifiers. J Chem Inf Model. 2006;46:193–200.
Neves, BJ, Braga, RC, Alves, VM, et al. Deep learning‐driven research for drug discovery: tackling malaria. PLoS Comput Biol. 2020;16(2):e1007025.
Ma, J, Sheridan, RP, Liaw, A, Dahl, GE, Svetnik, V. Deep neural nets as a method for quantitative structure–activity relationships. J Chem Inf Model. 2015;55(2):263–274.
Komatsu, R, Honda, M, Holzgrefe, HH, et al. Sensitivity of common marmosets to detect drug‐induced QT interval prolongation: moxifloxacin case study. J Pharmacol Toxicol Methods. 2010;61(3):271–276.
Deshpande, D, Pasipanodya, JG, Mpagama, SG, et al. Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug‐resistant tuberculosis. Clin Infect Dis. 2018;67(Suppl 3):S293–S302.
Casimiro‐Soriguer, CS, Loucera, C, Perez Florido, J, López‐López, D, Dopazo, J. Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples. Biol Direct. 2019;14(1):15.
Kabra, R, Ingale, P, Singh, S. Computationally designed synthetic peptides for transporter proteins imparts allostericity in Miltefosine resistant L. major. Biochem J. 2020;477(10):2007–2026.
Perryman, AL, Patel, JS, Russo, R, et al. Naïve Bayesian models for Vero cell cytotoxicity. Pharm Res. 2018;35:170.
Lane, T, Russo, DP, Zorn, KM, et al. Comparing and validating machine learning models for Mycobacterium tuberculosis drug discovery. Mol Pharm. 2018;15(10):4346–4360.
Dinić, J, Efferth, T, García‐Sosa, AT, et al. Repurposing old drugs to fight multidrug resistant cancers. Drug Resist Updat. 2020;52:100713100713. https://doi.org/10.1016/j.drup.2020.100713.
Hodos, RA, Kidd, BA, Shameer, K, Readhead, BP, Dudley, JT. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med. 2016;8(3):186–210.
Vilar, S, Uriarte, E, Santana, L, et al. Similarity‐based modeling in large‐scale prediction of drug‐drug interactions. Nat Protocols. 2014;9:2147–2163.
Guney, E. Reproducible drug repurposing: when similarity does not suffice. Pacific symposium on biocomputing. 2017;22:132–143.
Nabirotchkin, S, Peluffo, AE, Rinaudo, P, Yu, J, Hajj, R, Cohen, D. Next‐generation drug repurposing using human genetics and network biology. Curr Opin Pharmacol. 2020;51:78–92.
Visscher, PM, Wray, NR, Zhang, Q, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.
Pickrell, JK, Berisa, T, Liu, JZ, Ségurel, L, Tung, JY, Hinds, DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48(7):709–717.
Barabási, AL, Oltvai, ZN. Understanding the cell`s functional organization. Nat Rev Genet. 2004;5:101–113.
Rajkomar, A, Oren, E, Chen, K, et al. Scalable and accurate deep learning with electronic health records. npj Digit Med. 2018;1(1):18.
Zong, N, Kim, H, Ngo, V, Harismendy, O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations. Bioinformatics. 2017;33(15):2337–2344.
Ferrero, E, Brachat, S, Jenkins, JL, et al. Ten simple rules to power drug discovery with data science. PLoS Comput Biol. 2020;16(8):e1008126.
Piir, G, Kahn, I, García‐Sosa, AT, Sild, S, Ahte, P, Maran, U. Best practices for QSAR model reporting: physical and chemical properties, ecotoxicity, environmental fate, human health, and toxicokinetics endpoints. Environmental health perspectives. 2018;126:126001. https://dx.doi.org/10.1289/EHP3264.
Lowe, D. Another AI‐generated drug? | In the pipeline [Internet]; 2020. https://blogs.sciencemag.org/pipeline/archives/2020/01/31/another-ai-generated-drug. Accessed 09 Sep 2020.
Brown, N, Ertl, P, Lewis, R, Luksch, T, Reker, D, Schneider, N. Artificial intelligence in chemistry and drug design. J Comput Aided Mol Des. 2020;34:709–715.
Reyzin, L. Unprovability comes to machine learning. Nature. 2019;565(7738):166–167.
Brown, N, Fiscato, M, Segler, MHS, Vaucher, AC. GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model. 2019;59(3):1096–1108.
Muratov, EN, Bajorath, J, Sheridan, RP, et al. QSAR without borders. Chem Soc Rev. 2020;49:3525–3564.
Hutson, M. Core progress in AI has stalled in some fields. Science. 2020;368:927.
Bakhoum, M, Gallego, B, Mackenrodt, M, Surblytė‐Namavičienė, G. Personal data in competition, consumer protection and intellectual property law. Berlin: Springer, 2018.
Banterle, F. The interface between data protection and IP law: the case of trade secrets and the database sui generis right in marketing operations, and the ownership of raw data in big data analysis. Berlin, Heidelberg: Springer, 2018;p. 411–443.
How to lie with computational predictive models in drug discovery—DrugDiscovery.NET—AI in drug discovery [Internet]. http://www.drugdiscovery.net/2020/10/13/how-to-lie-with-computational-predictive-models-in-drug-discovery/. Accessed 16 Nov 2020.
Jiménez‐Luna, J, Grisoni, F, Schneider, G. Drug discovery with explainable artificial intelligence. Nat Mach Learn. 2020;2:573–584.
Horrobin, DF. Modern biomedical research: An internally self‐consistent universe with little contact with medical reality? Nat Rev Drug Discov. 2003;2(2):151–154.
Bickerton, GR, Paolini, GV, Besnard, J, Muresan, S, Hopkins, AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4(2):90–98.

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