Rethinking drug design in the artificial intelligence era

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Minds, brains, and programs

Searle, J. R. (1980) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00005756 · Cited by 6,954 · see citing works

Prototypical Networks for Few-shot LearningPreprint

Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 5,863 · see citing works

Neural Message Passing for Quantum ChemistryPreprint

Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works

Nonlinear Multiobjective Optimization

Miettinen, K. (1998) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4615-5563-6 · Cited by 4,746 · see citing works

How to improve R&D productivity: the pharmaceutical industry's grand challenge

Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566 · see citing works

On calibration of modern neural networks

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works

PubChem 2019 update: improved access to chemical data

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249 · see citing works

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works

The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates

Roughley, S. D., & Jordan, A. M. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200187y · Cited by 2,740 · see citing works

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works

A comprehensive map of molecular drug targets

Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427 · see citing works

The ChEMBL database in 2017

Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J. K., Méndez, D., Mutowo, P., Atkinson, F. L., Bellis, L. J., Cibrián–Uhalte, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,351 · see citing works

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847 · see citing works

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813 · see citing works

Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W.-G., Barzilay, R., & Jaakkola, T. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1802.04364 · Cited by 1,783 · see citing works

Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR

Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1012-2 · Cited by 1,756 · see citing works

Adversarial Examples: Attacks and Defenses for Deep Learning

Yuan, X., He, P., Zhu, Q., & Li, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886017 · Cited by 1,733 · see citing works

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works

Organic synthesis provides opportunities to transform drug discovery

Blakemore, D. C., Castro, L., Churcher, I., Rees, D. C., Thomas, A. W., Wilson, D. M., & Wood, A. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0021-z · Cited by 1,495 · see citing works

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,379 · see citing works

A Primer on Neural Network Models for Natural Language Processing

Goldberg, Y. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4992 · Cited by 1,259 · see citing works

Organs-on-chips at the frontiers of drug discovery

Esch, E. W., Bahinski, A., & Huh, D. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4539 · Cited by 1,185 · see citing works

The role of ligand efficiency metrics in drug discovery

Hopkins, A. L., Keserü, G. M., Leeson, P. D., Rees, D. C., & Reynolds, C. H. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4163 · Cited by 1,181 · see citing works

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

Ultra-large library docking for discovering new chemotypes

Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998 · see citing works

Computer-based de novo design of drug-like molecules

Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works

Opportunities and challenges in phenotypic drug discovery: an industry perspective

Moffat, J. G., Vincent, F., Lee, J. A., Eder, J., & Prunotto, M. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.111 · Cited by 939 · see citing works

Automating drug discovery

Schneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910 · cited 1× · see citing works

Low Data Drug Discovery with One-Shot Learning

Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799 · see citing works

Artificial intelligence in medical practice: the question to the answer?

Miller, D. D., & Brown, E. W. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.10.035 · Cited by 774 · see citing works

Machine Learning in Computer-Aided Synthesis Planning

Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704 · see citing works

Novel trends in high-throughput screening

Mayr, L. M., & Bojanic, D. (2009) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2009.08.004 · Cited by 690 · see citing works

The automation of science

King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L. N., et al. (2009) · Science · doi:10.1126/science.1165620 · Cited by 679 · see citing works

Deep neural network improves fracture detection by clinicians

Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A. S., Mozer, M. C., Sicular, S., Hanel, D. P., Gardner, M. J., Gupta, A., Hotchkiss, R. J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806905115 · Cited by 654 · see citing works

Organic synthesis in a modular robotic system driven by a chemical programming language

Steiner, S., Wolf, J. B., Glatzel, S., Andreou, A., Granda, J. M., Keenan, G., Hinkley, T., Aragón-Camarasa, G., Kitson, P. J., Angelone, D., et al. (2018) · Science · doi:10.1126/science.aav2211 · Cited by 630 · see citing works

Computer‐Assisted Synthetic Planning: The End of the Beginning

Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611 · see citing works

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610 · see citing works

Computer-Aided Drug Design Methods

Yu, W., & MacKerell, A. D. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6634-9_5 · Cited by 599 · see citing works

Executable cell biology

Fisher, J., & Henzinger, T. A. (2007) · Nature Biotechnology · doi:10.1038/nbt1356 · Cited by 543 · see citing works

Predicting drug metabolism: experiment and/or computation?

Kirchmair, J., Göller, A. H., Lang, D. H., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4581 · Cited by 507 · see citing works

Neural Networks in Chemistry

Gasteiger, J., & Zupan, J. (1993) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199305031 · Cited by 503 · see citing works

Expanding the medicinal chemistry synthetic toolbox

Boström, J., Brown, D. G., Young, R., & Keserű, G. (2018) · Nature reviews. Drug discovery · doi:10.1038/nrd.2018.116 · Cited by 498 · see citing works

Reconfigurable system for automated optimization of diverse chemical reactions

Bédard, A., Adamo, A., Aroh, K. C., Russell, M. G., Bedermann, A. A., Torosian, J., Yue, B., Jensen, K. F., & Jamison, T. F. (2018) · Science · doi:10.1126/science.aat0650 · Cited by 488 · see citing works

Generative Recurrent Networks for De Novo Drug Design

Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201700111 · Cited by 474 · see citing works

Accurate Uncertainties for Deep Learning Using Calibrated RegressionPreprint

Kuleshov, V., Fenner, N., & Ermon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00263 · Cited by 455 · see citing works

“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

Schwaller, P., Gaudin, T., Lányi, D., Bekas, C., & Laino, T. (2018) · Chemical Science · doi:10.1039/c8sc02339e · Cited by 449 · see citing works

Computer Aided Drug Design: Success and Limitations

Baig, M. H., Ahmad, K., Roy, S., Ashraf, J. M., Adil, M., Siddiqui, M. H., Khan, S. A., Kamal, M. A., Provazník, I., & Choi, I. (2016) · Current Pharmaceutical Design · doi:10.2174/1381612822666151125000550 · Cited by 426 · see citing works

Application of Generative Autoencoder in <i>De Novo</i> Molecular Design

Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411 · see citing works

Artificial Intelligence in Drug Design

Heßler, G., & Baringhaus, K. H. (2018) · Molecules · doi:10.3390/molecules23102520 · Cited by 387 · see citing works

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380 · see citing works

<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence

Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201700153 · Cited by 373 · see citing works

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Korotcov, A. V., Tkachenko, V., Russo, D. P., & Ekins, S. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00578 · Cited by 349 · see citing works

Chemical space as a source for new drugs

Reymond, J., van Deursen, R., Blum, L. C., & Ruddigkeit, L. (2010) · MedChemComm · doi:10.1039/c0md00020e · Cited by 344 · see citing works

Comparability of Mixed IC50 Data – A Statistical Analysis

Kalliokoski, T., Krämer, C., Vulpetti, A., & Gedeck, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061007 · Cited by 317 · see citing works

Molecule-pharmacophore superpositioning and pattern matching in computational drug design

Wolber, G., Seidel, T., Bendix, F., & Langer, T. (2007) · Drug Discovery Today · doi:10.1016/j.drudis.2007.09.007 · Cited by 315 · see citing works

Chemical predictive modelling to improve compound quality

Cumming, J. G., Davis, A. M., Mureşan, S., Haeberlein, M., & Chen, H. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4128 · Cited by 309 · see citing works

Opportunities and obstacles for deep learning in biology and medicinePreprint

Ching, T. H., Himmelstein, D. S., Beaulieu‐Jones, B. K., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142760 · Cited by 308 · see citing works

Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation

Fourches, D., Muratov, E., & Tropsha, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00129 · Cited by 294 · see citing works

Matched Molecular Pairs as a Medicinal Chemistry Tool: Miniperspective

Griffen, E., Leach, A. G., Robb, G. R., & Warner, D. J. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200452d · Cited by 282 · see citing works

Trends in clinical success rates

Smietana, K., Siatkowski, M., & Møller, M. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.85 · Cited by 277 · cited 2× · see citing works

Chemical Space of DNA-Encoded Libraries

Franzini, R. M., & Randolph, C. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01874 · Cited by 274 · see citing works

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

Huang, R., Xia, M., Sakamuru, S., Zhao, J., Shahane, S. A., Attene‐Ramos, M. S., Zhao, T., Austin, C. P., & Simeonov, A. M. (2016) · Nature Communications · doi:10.1038/ncomms10425 · Cited by 259 · see citing works

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002380 · Cited by 251 · see citing works

Improving Drug Design: An Update on Recent Applications of Efficiency Metrics, Strategies for Replacing Problematic Elements, and Compounds in Nontraditional Drug Space

Meanwell, N. A. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00043 · Cited by 244 · see citing works

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance PrinciplesPreprint

Ollivier, Y., Arnold, L., Auger, A., & Hansen, N. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.3708 · Cited by 221 · see citing works

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

Gao, Z., Wang, L., Zhou, L., & Zhang, J. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2526603 · Cited by 220 · see citing works

Nanoscale synthesis and affinity ranking

Gesmundo, N. J., Sauvagnat, B., Curran, P. J., Richards, M. P., Andrews, C. L., Dandliker, P. J., & Cernak, T. (2018) · Nature · doi:10.1038/s41586-018-0056-8 · Cited by 214 · see citing works

2018 FDA drug approvals

Mullard, A. (2019) · Nature Reviews Drug Discovery · doi:10.1038/d41573-019-00014-x · Cited by 214 · see citing works

Artificial neural networks for computer-based molecular design

Schneider, G., & Wrede, P. (1998) · Progress in Biophysics and Molecular Biology · doi:10.1016/s0079-6107(98)00026-1 · Cited by 213 · see citing works

Computer-assisted drug design

Hopfinger, A. J. (1985) · Journal of Medicinal Chemistry · doi:10.1021/jm00147a001 · Cited by 212 · see citing works

Multi-objective optimization methods in drug design

Nicolaou, C. A., & Brown, N. (2013) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2013.02.001 · Cited by 196 · see citing works

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

Trobe, M., & Burke, M. D. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201710482 · Cited by 194 · see citing works

Applications of chemogenomic library screening in drug discovery

Jones, L. H., & Bunnage, M. E. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.244 · Cited by 192 · see citing works

Target discovery from data mining approaches

Yang, Y., Adelstein, S. J., & Kassis, A. I. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.12.005 · Cited by 190 · see citing works

Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment

Wetmore, B. A. (2014) · Toxicology · doi:10.1016/j.tox.2014.05.012 · Cited by 175 · see citing works

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

Segall, M. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212799436430 · Cited by 174 · see citing works

DNA ‐encoded chemical libraries – achievements and remaining challenges

Favalli, N., Bassi, G., Scheuermann, J., & Neri, D. (2018) · FEBS Letters · doi:10.1002/1873-3468.13068 · Cited by 174 · see citing works

Activity, assay and target data curation and quality in the ChEMBL database

Papadatos, G., Gaulton, A., Hersey, A., & Overington, J. P. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-015-9860-5 · Cited by 169 · see citing works

Automated De Novo Drug Design: Are We Nearly There Yet?

Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201814681 · Cited by 168 · see citing works

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions

Tang, J. P., Tanoli, Z., Ravikumar, B., Alam, Z., Rebane, A., Vähä-Koskela, M. J. V., Peddinti, G., van Adrichem, A. J., Wakkinen, J., Jaiswal, A., et al. (2017) · Cell chemical biology · doi:10.1016/j.chembiol.2017.11.009 · Cited by 154 · see citing works

De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

Sattarov, B., Baskin, I. I., Horvath, D., Marcou, G., Bjerrum, E. J., & Varnek, A. A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00751 · Cited by 148 · see citing works

Data-driven medicinal chemistry in the era of big data

Lusher, S. J., McGuire, R., van Schaik, R. C., Nicholson, C. D., & de Vlieg, J. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.12.004 · Cited by 146 · see citing works

Biotechnology, Big Data and Artificial Intelligence

Oliveira, A. L. (2019) · Biotechnology Journal · doi:10.1002/biot.201800613 · Cited by 145 · see citing works

Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Merk, D., Grisoni, F., Friedrich, L., & Schneider, G. (2018) · Communications Chemistry · doi:10.1038/s42004-018-0068-1 · Cited by 136 · see citing works

Deriving genomic diagnoses without revealing patient genomes

Jagadeesh, K. A., Wu, D. J., Birgmeier, J. A., Boneh, D., & Bejerano, G. (2017) · Science · doi:10.1126/science.aam9710 · Cited by 134 · see citing works

FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

Knudsen, T. B., Keller, D. A., Sander, M., Carney, E. W., Doerrer, N. G., Eaton, D. L., Fitzpatrick, S. C., Hastings, K. L., Mendrick, D. L., Tice, R. R., et al. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfu234 · Cited by 133 · see citing works

Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle

Plowright, A. T., Johnstone, C., Kihlberg, J. O., Pettersson, J. A., Robb, G. R., & Thompson, R. A. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.09.012 · Cited by 132 · see citing works

High-Throughput Automation in Chemical Process Development

Selekman, J. A., Qiu, J. X., Tran, K., et al. (2017) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-060816-101411 · Cited by 127 · see citing works

Healthy skepticism: assessing realistic model performance

Brown, S. P., Muchmore, S. W., & Hajduk, P. J. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.01.012 · Cited by 126 · see citing works

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Rao, M. S., Gupta, R., Liguori, M. J., Hu, M., Huang, X., Mantena, S. R., Mittelstadt, S. W., Blomme, E. A. G., & Vleet, T. R. V. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00025 · Cited by 122 · see citing works

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (fromytox)

Miyao, T., Kaneko, H., & Funatsu, K. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00628 · Cited by 113 · see citing works

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

Cavalluzzi, M. M., Mangiatordi, G. F., Nicolotti, O., & Lentini, G. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1365056 · Cited by 104 · see citing works

Structural Mechanisms Determining Inhibition of the Collagen Receptor DDR1 by Selective and Multi-Targeted Type II Kinase Inhibitors

Canning, P., Tan, L., Chu, K., Lee, S. W., Gray, N. S., & Bullock, A. N. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.04.014 · Cited by 103 · see citing works

Autonomous Molecular Design: Then and Now

Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials &amp; Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103 · see citing works

Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

Button, A. L., Merk, D., Hiss, J. A., & Schneider, G. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0067-7 · Cited by 103 · see citing works

Evolving molecules using multi-objective optimization: applying to ADME/Tox

Ekins, S., Honeycutt, J. D., & Metz, J. T. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.04.003 · Cited by 102 · see citing works

Active Learning for Computational Chemogenomics

Reker, D., Schneider, P., Schneider, G., & Brown, J. B. (2017) · Future Medicinal Chemistry · doi:10.4155/fmc-2016-0197 · Cited by 101 · see citing works

Chemical Space Mimicry for Drug Discovery

Yuan, W., Jiang, D., Nambiar, D. K., Liew, L. P., Hay, M. P., Bloomstein, J. D., Lu, P., Turner, B. E., Le, Q., Tibshirani, R. J., et al. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00754 · Cited by 100 · see citing works

De novodesign: balancing novelty and confined chemical space

Kutchukian, P. S., & Shakhnovich, E. I. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.497534 · Cited by 92 · see citing works

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

McDonagh, J. L., Nath, N., Ferrari, L. D., van Mourik, T., & Mitchell, J. B. O. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci4005805 · Cited by 90 · see citing works

Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization

Reutlinger, M., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201310864 · Cited by 90 · see citing works

Do Animal Models of Acute Pancreatitis Reproduce Human Disease?

Gorelick, F. S., & Lerch, M. M. (2017) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2017.05.007 · Cited by 86 · see citing works

Why and how have drug discovery strategies in pharma changed? What are the new mindsets?

Mignani, S. M., Huber, S., Tomás, H., Rodrigues, J., & Majoral, J. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.09.007 · Cited by 83 · see citing works

Integrated Platform for Expedited Synthesis–Purification–Testing of Small Molecule Libraries

Baranczak, A., Tu, N. P., Marjanovic, J., Searle, P. A., Vasudevan, A., & Djurić, S. W. (2017) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.7b00054 · Cited by 83 · see citing works

Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors

Reker, D., Schneider, P., & Schneider, G. (2016) · Chemical Science · doi:10.1039/c5sc04272k · Cited by 81 · see citing works

Robots and the return to collaborative intelligence

Goldberg, K. Y. (2018) · Nature Machine Intelligence · doi:10.1038/s42256-018-0008-x · Cited by 81 · see citing works

Review of diagnostic error in anatomical pathology and the role and value of second opinions in error prevention

Peck, M., Moffat, D. F. C., Latham, B. I., et al. (2018) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2018-205226 · Cited by 79 · see citing works

Drug Discovery Goes Three-Dimensional: Goodbye to Flat High-Throughput Screening?

Eglen, R. M., & Randle, D. H. (2015) · Assay and Drug Development Technologies · doi:10.1089/adt.2015.647 · Cited by 78 · see citing works

The Multiple Common Point Set Problem and Its Application to Molecule Binding Pattern Detection

Shatsky, M., Shulman‐Peleg, A., Nussinov, R., et al. (2006) · Journal of Computational Biology · doi:10.1089/cmb.2006.13.407 · Cited by 71 · see citing works

Big Data in Drug Discovery

Brown, N., Cambruzzi, J., Cox, P. J., Davies, M., Dunbar, J., Plumbley, D., Sellwood, M. A., Sim, A., Williams-Jones, B. I., Zwierzyna, M., et al. (2018) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2017.12.003 · Cited by 71 · see citing works

Drug target ontology to classify and integrate drug discovery data

Lin, Y., Mehta, S., Küçük‐McGinty, H., Turner, J. P., Vidović, D., Forlin, M., Koleti, A., Nguyễn, Ð.-T., Jensen, L. J., Guha, R., et al. (2017) · Journal of Biomedical Semantics · doi:10.1186/s13326-017-0161-x · Cited by 69 · see citing works

Making medicinal chemistry more effective—application of Lean Sigma to improve processes, speed and quality

Andersson, S., Armstrong, A., Björe, A., Bowker, S., Chapman, S. F., Davies, R., Donald, C. S., Egner, B. J., Elebring, T., Holmqvist, S., et al. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.03.005 · Cited by 66 · see citing works

The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

Cases, M., Briggs, K. A., Steger‐Hartmann, T., Pognan, F., Marc, P., Kleinöder, T., Schwab, C. H., Pastor, M., Wichard, J., & Sanz, F. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms151121136 · Cited by 65 · see citing works

Ligand efficiency metrics considered harmful

Kenny, P. W., Leitão, A., & Montanari, C. A. (2014) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9757-8 · Cited by 62 · see citing works

Size estimation of chemical space: how big is it?

Drew, K. L. M., Baiman, H., Khwaounjoo, P., Yu, B., & Reynisson, J. (2011) · Journal of Pharmacy and Pharmacology · doi:10.1111/j.2042-7158.2011.01424.x · Cited by 59 · see citing works

Practical application of ligand efficiency metrics in lead optimisation

Scott, J. S., & Waring, M. J. (2018) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2018.04.004 · Cited by 56 · see citing works

A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

Wong, W. W. L., & Burkowski, F. J. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-4 · Cited by 53 · see citing works

An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer — against all odds?

Follmann, M., Briem, H., Steinmeyer, A., Hillisch, A., Schmitt, M. H., Haning, H., & Meier, H. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.12.003 · Cited by 53 · see citing works

Using Anchors to Estimate Clinical State without Labeled Data.

Halpern, Y., Choi, Y., Horng, S., & Sontag, D. A. (2014) · PubMed Central · Cited by 52 · see citing works

Enhance Visual Recognition Under Adverse Conditions via Deep Networks

Liu, D., Cheng, B., Wang, Z., Zhang, H., & Huang, T. S. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2908802 · Cited by 51 · see citing works

Artificial Intelligence in Drug Design—The Storm Before the Calm?

Jordan, A. M. (2018) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.8b00500 · Cited by 50 · see citing works

Neural networks are useful tools for drug design

Schneider, G. (2000) · Neural Networks · doi:10.1016/s0893-6080(99)00094-5 · Cited by 49 · see citing works

Strategies of multi-objective optimization in drug discovery and development

Nicolotti, O., Giangreco, I., Introcaso, A., Leonetti, F., Stefanachi, A., & Carotti, A. (2011) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2011.588696 · Cited by 49 · see citing works

Scientific competency questions as the basis for semantically enriched open pharmacological space development

Azzaoui, K., Jacoby, E., Senger, S., Rodríguez, E. C., Loza, M. I., Zdrazil, B., Pinto, M., Williams, A. J., de la Torre, V., Mestres, J., et al. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.05.008 · Cited by 48 · see citing works

Which aspects of HTS are empirically correlated with downstream success?

Bender, A., Bojanic, D., Davies, J. W., et al. (2008) · PubMed · Cited by 48 · see citing works

Multi-Objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment

Shim, V. A., Tan, K. C., Chia, J. Y., & Mamun, A. A. (2012) · Evolutionary Computation · doi:10.1162/evco_a_00066 · Cited by 47 · see citing works

[29] Computer-assisted rational drug design

Martin, Y. C. (1991) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/0076-6879(91)03031-b · Cited by 45 · see citing works

The Purchasable Chemical Space: A Detailed Picture

Lucas, X., Grüning, B. A., Bleher, S., & Günther, S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00116 · Cited by 45 · see citing works

Drug discovery chemistry: a primer for the non-specialist

Jordan, A. M., & Roughley, S. D. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.04.005 · Cited by 45 · see citing works

Data Integrity: History, Issues, and Remediation of Issues

Rattan, A. K. (2017) · PDA Journal of Pharmaceutical Science and Technology · doi:10.5731/pdajpst.2017.007765 · Cited by 45 · see citing works

Medicinal Chemistry for 2020

Satyanarayanajois, S. D., & Hill, R. A. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.135 · Cited by 43 · see citing works

Design, Synthesis, and Testing of Potent, Selective Hepsin Inhibitors via Application of an Automated Closed-Loop Optimization Platform

Pant, S. M., Mukonoweshuro, A., Desai, B. K., Ramjee, M. K., Selway, C. N., Tarver, G. J., Wright, A. G., Birchall, K., Chapman, T. M., Tervonen, T. A., et al. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b01698 · Cited by 43 · see citing works

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Silverbush, D., Grosskurth, S. E., Wang, D. Y. Q., Powell, F., Göttgens, B., Dry, J. R., & Fisher, J. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-1578 · Cited by 42 · see citing works

Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands

Rodrigues, T., Hauser, N., Reker, D., Reutlinger, M., Wunderlin, T., Hamon, J., Koch, G., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201410201 · Cited by 41 · see citing works

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose–response data

Mpindi, J.-P., Potdar, S., Bychkov, D., Saarela, J., Saeed, K., Wennerberg, K., Aittokallio, T. A., Östling, P., & Kallioniemi, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv455 · Cited by 39 · see citing works

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki, T., Watanabe, R., Kawashima, H., Ohashi, R., Natsume‐Kitatani, Y., Nagao, C., & Mizuguchi, K. (2018) · Molecular Informatics · doi:10.1002/minf.201800086 · Cited by 38 · see citing works

Design of Natural‐Product‐Inspired Multitarget Ligands by Machine Learning

Grisoni, F., Merk, D., Friedrich, L., & Schneider, G. (2019) · ChemMedChem · doi:10.1002/cmdc.201900097 · Cited by 38 · see citing works

Generative Models for Artificially‐intelligent Molecular Design

Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201880131 · Cited by 37 · see citing works

Quantitative high-throughput screening data analysis: challenges and recent advances

Shockley, K. R. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.10.005 · Cited by 36 · see citing works

Closing the Loop: Developing an Integrated Design, Make, and Test Platform for Discovery

Parry, D. M. (2019) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.9b00095 · Cited by 35 · see citing works

Macromolecular target prediction by self-organizing feature maps

Schneider, G., & Schneider, P. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1274727 · Cited by 34 · see citing works

Challenges with multi-objective QSAR in drug discovery

Lambrinidis, G., & Tsantili‐Kakoulidou, A. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1496079 · Cited by 34 · see citing works

Compound Libraries: Recent Advances and Their Applications in Drug Discovery

Gong, Z., Hu, G., Li, Q., et al. (2017) · Current Drug Discovery Technologies · doi:10.2174/1570163814666170425155154 · Cited by 33 · see citing works

Advances in Lead Generation

Holenz, J., & Stoy, P. (2018) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2018.12.001 · Cited by 33 · see citing works

A reliable computational workflow for the selection of optimal screening libraries

Gilad, Y., Nadassy, K., & Senderowitz, H. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0108-0 · Cited by 23 · see citing works

O-alkylhomoserine synthesis from O-acetylhomoserine and alcohol

Murooka, Y., Seto, K., & Harada, T. (1970) · Biochemical and Biophysical Research Communications · doi:10.1016/0006-291x(70)90519-x · Cited by 22 · see citing works

Occurrence and significance of lymphopenia in sarcoidosis.

HOFFBRAND, B. I. (1968) · PubMed · doi:10.1164/arrd.1968.98.1.107 · Cited by 22 · see citing works

Designing Combinatorial Libraries Optimized on Multiple Objectives

Gillet, V. J. (2004) · Methods in molecular biology · doi:10.1385/1-59259-802-1:335 · Cited by 21 · see citing works

Accelerating Drug Discovery by Integrative Implementation of Laboratory Automation in the Work Flow

Nettekoven, M. H., & Thomas, A. W. (2002) · Current Medicinal Chemistry · doi:10.2174/0929867023368764 · Cited by 19 · see citing works

Gaussian Process Prior Variational AutoencodersPreprint

Casale, F. P., Dalca, A. V., Saglietti, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11738 · Cited by 19 · see citing works

Enabling Chemistry Technologies and Parallel Synthesis—Accelerators of Drug Discovery Programmes

Vasudevan, A., Bogdan, A. R., Koolman, H. F., Wang, Y., & Djurić, S. W. (2017) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2016.11.001 · Cited by 17 · see citing works

Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database

Mazzolari, A., Afzal, A. M., Pedretti, A., Testa, B., Vistoli, G., & Bender, A. (2019) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.8b00603 · Cited by 17 · see citing works

Is one-shot learning a viable option in drug discovery?

Baskin, I. I. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1593368 · Cited by 17 · see citing works

A view of estimation of distribution algorithms through the lens of expectation-maximization

Brookes, D. H., Busia, A., Fannjiang, C., Murphy, K., & Listgarten, J. (2020) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3377929.3389938 · Cited by 17 · see citing works

Topliss Batchwise Schemes Reviewed in the Era of Open Data Reveal Significant Differences between Enzymes and Membrane Receptors

Richter, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00195 · Cited by 15 · see citing works

Extending accessible chemical space for the identification of novel leads

Bajorath, J. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1210126 · Cited by 14 · see citing works

Selection of Informative Examples in Chemogenomic Datasets

Reker, D., & Brown, J. B. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8639-2_13 · Cited by 13 · see citing works

Hereditary disorders of blood coagulation due to defective and deficient synthesis of protein.

Jackson, D. P. (1971) · PubMed · Cited by 12 · see citing works

Stuck in a rut with old chemistry

Boström, J., & Brown, D. G. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.02.017 · Cited by 12 · see citing works

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Bruns, D., Merk, D., Kumar, K. S., et al. (2019) · ChemistryOpen · doi:10.1002/open.201900222 · Cited by 12 · see citing works

Extending ‘Predict First’ to the Design-Make-Test Cycle in Small-Molecule Drug Discovery

Harrison, S. T., Lahue, B. R., Peng, Z., Donofrio, A. E., Chang, C., & Glick, M. (2017) · Future Medicinal Chemistry · doi:10.4155/fmc-2017-0025 · Cited by 11 · see citing works

Make Way for Robot Scientists

King, R. D., Rowland, J. J., Oliver, S. G., et al. (2009) · Science · doi:10.1126/science.325_945a · Cited by 10 · see citing works

Toxoplasmosis in India: prevalence of antibodies in camels

Gill, H. S., & Prakash, O. (1969) · Annals of Tropical Medicine and Parasitology · doi:10.1080/00034983.1969.11686626 · Cited by 10 · see citing works

High Throughput Screening Methods

(2016) · Chemical biology · doi:10.1039/9781782626770 · Cited by 10 · see citing works

Adrenal cortical dysfunction in leprosy.

Mb, G., & Fg, S. (1970) · PubMed · Cited by 8 · see citing works

Guest Editorial: Critical Thinking in Education and Research—Why and How?

Sieroka, N., Otto, V. I., & Folkers, G. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201810397 · Cited by 8 · see citing works

Is target validation all we need?

Frigault, M. M., & Barrett, J. C. (2014) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2014.09.004 · Cited by 7 · see citing works

Phosphodiesterase induction in Dictyostelium discoideum by inhibition of extracellular phosphodiesterase activity

Haastert, P. J. M. V., Bijleveld, W., & Konijn, T. M. (1982) · Developmental Biology · doi:10.1016/0012-1606(82)90087-2 · Cited by 5 · see citing works

Inhibition of giant cell formation by compound 48/80 after infection with herpesvirus hominis

Falke, D., Just, I., & Strecker, W. (1974) · Archives of Virology · doi:10.1007/bf01240212 · Cited by 5 · see citing works

[Studies of gait in patients].

Debrunner, H. U., & Mäder, G. E. (1972) · PubMed · Cited by 4 · see citing works

AI could threaten pharmaceutical patents

Heuer, L. (2018) · Nature · doi:10.1038/d41586-018-05555-6 · Cited by 4 · see citing works

Recommendations for chemists

Rohall, S. L., Pancost-Heidebrecht, M., Shirley, B., et al. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240376 · Cited by 3 · see citing works

[Experiments on the photometric determination of the intracellular pH value in skeletal muscle].

Herbst, M., & Piontek, P. (1969) · PubMed · Cited by 2 · see citing works

[On the technic of blood cultures at the sickbed].

Stille, W. (1966) · PubMed · Cited by 2 · see citing works

AEROSOL CONTAMINATION BY SOLID CARBON DIOXIDE

WEBSTER, A. F. (1973) · Australian Veterinary Journal · doi:10.1111/j.1751-0813.1973.tb06750.x · Cited by 1 · see citing works

[Movement of the soft palate in articulation (fiberscopic observation)].

Ushijima, T., & Sawashima, M. (1972) · PubMed · Cited by 1 · see citing works

[A case of true aneurysm of the median meningeal artery].

Cristi, G., Dettori, P., GAIST, G., et al. (1971) · PubMed · Cited by 1 · see citing works

Dilemma over AI and drug patenting already under debate

King, R. D., & Courtney, P. (2018) · Nature · doi:10.1038/d41586-018-05955-8 · Cited by 1 · see citing works

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works

Explainable machine learning for scientific insights and discoveries

Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040 · see citing works

Using artificial intelligence in academic writing and research: An essential productivity tool

Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580 · see citing works

Generative artificial intelligence empowers digital twins in drug discovery and clinical trials

Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2273839 · Cited by 235 · see citing works

Artificial Intelligence for Drug Discovery: Are We There Yet?

Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works

Accelerating antibiotic discovery through artificial intelligence

Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234 · see citing works

Artificial intelligence foundation for therapeutic science

Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01131-2 · Cited by 207 · see citing works

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances · doi:10.1126/sciadv.abg3338 · Cited by 190 · see citing works

Artificial intelligence in chemistry and drug design

Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works

Explainable artificial intelligence for drug discovery and development: a comprehensive survey

Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124 · see citing works

RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design

Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99 · see citing works

Link-INVENT: Generative Linker Design with Reinforcement Learning

Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · doi:10.26434/chemrxiv-2022-qkx9f · Cited by 101 · see citing works

Repurposing drugs to treat cardiovascular disease in the era of precision medicine

Abdelsayed, M., Kort, E. J., Jovinge, S., & Mercola, M. (2022) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00717-6 · Cited by 93 · see citing works

The machine learning life cycle and the cloud: implications for drug discovery

Spjuth, O., Frid, J., & Hellander, A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1932812 · Cited by 67 · see citing works

The Potential Applications of Artificial Intelligence in Drug Discovery and Development

Farghali, H., Kutinová Canová, N., & Arora, M. (2021) · Physiological Research · doi:10.33549/physiolres.934765 · Cited by 72 · see citing works

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

Floresta, G., Zagni, C., Gentile, D., Patamia, V., & Rescifina, A. (2022) · doi:10.20944/preprints202203.0032.v1 · Cited by 67 · see citing works

Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61 · see citing works

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer’s disease

Cheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., Haines, J. L., Leverenz, J. B., Gan, L., Hu, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101379 · Cited by 63 · see citing works

Artificial intelligence for dementia drug discovery and trials optimization

Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's &amp; Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

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Hawkins, D. (2017)

Ancient Scepticism. By Harald Thorsrud. Berkeley, CA: University of California Press, 2009. Pp. 248 + xvi. ISBN: 978-0-520-26026-9.

Grgić, F. (2013) · International Journal for the Study of Skepticism · doi:10.1163/22105700-03021111

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Hupp, S. D. A. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009000611.022

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(2019) · BRILL eBooks · doi:10.1163/9789004393530 · Cited by 8 · see citing works