Automating drug discovery

Deep learning

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

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

Solid Phase Peptide Synthesis. I. The Synthesis of a Tetrapeptide

Merrifield, R. B. (1963) · Journal of the American Chemical Society · doi:10.1021/ja00897a025 · Cited by 7,825

In-Datacenter Performance Analysis of a Tensor Processing Unit

Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537

Microfluidics: Fluid physics at the nanoliter scale

Squires, T. M., & Quake, S. R. (2005) · Reviews of Modern Physics · doi:10.1103/revmodphys.77.977 · Cited by 4,507

Distilling Free-Form Natural Laws from Experimental Data

Schmidt, M., & Lipson, H. (2009) · Science · doi:10.1126/science.1165893 · Cited by 3,327

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 2,768

Target-Oriented and Diversity-Oriented Organic Synthesis in Drug Discovery

Schreiber, S. L. (2000) · Science · doi:10.1126/science.287.5460.1964 · Cited by 2,571

The influence of drug-like concepts on decision-making in medicinal chemistry

Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403

Quantifying the chemical beauty of drugs

Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,397

Analysis of Past and Present Synthetic Methodologies on Medicinal Chemistry: Where Have All the New Reactions Gone?

Brown, D. G., & Boström, J. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01409 · Cited by 2,047

Diagnosing the decline in pharmaceutical R&D efficiency

Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3681 · Cited by 1,981

Lab-on-a-chip: microfluidics in drug discovery

Dittrich, P. S., & Manz, A. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd1985 · Cited by 1,852

Can we open the black box of AI?

Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773

Function-Oriented Synthesis, Step Economy, and Drug Design

Wender, P. A., Verma, V. A., Paxton, T. J., & Pillow, T. H. (2007) · Accounts of Chemical Research · doi:10.1021/ar700155p · Cited by 1,215

Lessons from 60 years of pharmaceutical innovation

Munos, B. H. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2961 · Cited by 1,208

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

Microfluidic platforms for lab-on-a-chip applications

Haeberle, S., & Zengerle, R. (2007) · Lab on a Chip · doi:10.1039/b706364b · Cited by 1,126

Ultrahigh-throughput screening in drop-based microfluidics for directed evolution

Agresti, J., Antipov, E., Abate, A., Ahn, K., Rowat, A., Baret, J., Márquez, M., Klibanov, A., Griffiths, A., & Weitz, D. (2010) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0910781107 · Cited by 1,121

Profound Methyl Effects in Drug Discovery and a Call for New CH Methylation Reactions

Schönherr, H., & Cernak, T. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201303207 · Cited by 1,104

Microdroplets in Microfluidics: An Evolving Platform for Discoveries in Chemistry and Biology

Theberge, A. B., Courtois, F., Schaerli, Y., Fischlechner, M., Abell, C., Hollfelder, F., & Huck, W. T. S. (2010) · Angewandte Chemie International Edition · doi:10.1002/anie.200906653 · Cited by 1,059

Deciding Whether To Go with the Flow: Evaluating the Merits of Flow Reactors for Synthesis

Hartman, R. L., McMullen, J. P., & Jensen, K. F. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201004637 · Cited by 1,005

Robots that can adapt like animals

Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015) · Nature · doi:10.1038/nature14422 · Cited by 979

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

On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system

Adamo, A., Beingessner, R. L., Behnam, M., Chen, J., Jamison, T. F., Jensen, K. F., Monbaliu, J. M., Myerson, A. S., Revalor, E., Snead, D. R., et al. (2016) · Science · doi:10.1126/science.aaf1337 · Cited by 946

Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems

Vermesan, O., & Friess, P. (2013) · Cited by 873

Large-scale de novo DNA synthesis: technologies and applications

Kosuri, S., & Church, G. M. (2014) · Nature Methods · doi:10.1038/nmeth.2918 · Cited by 869

Protein–Ligand Scoring with Convolutional Neural Networks

Ragoza, M., Hochuli, J. E., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 853

Automated design of ligands to polypharmacological profiles

Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832

Deep learning for computational chemistry

Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809

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

Synthesis and medical applications of oligosaccharides

Seeberger, P. H., & Werz, D. B. (2007) · Nature · doi:10.1038/nature05819 · Cited by 763

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760

Prediction of Organic Reaction Outcomes Using Machine Learning

Coley, C. W., Barzilay, R., Jaakkola, T., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00064 · Cited by 755

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701

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

Flow chemistry syntheses of natural products

Pastre, J. C., Browne, D. L., & Ley, S. V. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60246j · Cited by 664

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

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

Neural networks: A new method for solving chemical problems or just a passing phase?

Zupan, J., & Gasteiger, J. (1991) · Analytica Chimica Acta · doi:10.1016/s0003-2670(00)80865-x · Cited by 581

Multi-step continuous-flow synthesis

Britton, J., & Raston, C. L. (2017) · Chemical Society Reviews · doi:10.1039/c6cs00830e · Cited by 555

Synthesis of many different types of organic small molecules using one automated process

Li, J., Ballmer, S. G., Gillis, E. P., Fujii, S., Schmidt, M., Ray, A. M. E. P., Lehmann, J. W., Morehouse, G. F., & Burke, M. D. (2015) · Science · doi:10.1126/science.aaa5414 · Cited by 545

Synthesis of Cesium Lead Halide Perovskite Nanocrystals in a Droplet-Based Microfluidic Platform: Fast Parametric Space Mapping

Lignos, I. G., Stavrakis, S., Nedelcu, G., Proteşescu, L., deMello, A. J., & Kovalenko, M. V. (2016) · Nano Letters · doi:10.1021/acs.nanolett.5b04981 · Cited by 538

Flash Chemistry: Fast Chemical Synthesis by Using Microreactors

Yoshida, J., Nagaki, A., & Yamada, T. (2008) · Chemistry - A European Journal · doi:10.1002/chem.200800582 · Cited by 537

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 523

Selective Encapsulation of Single Cells and Subcellular Organelles into Picoliter- and Femtoliter-Volume Droplets

He, M., Edgar, J. S., Jeffries, G. D. M., Lorenz, R. M., Shelby, J. P., & Chiu, D. T. (2005) · Analytical Chemistry · doi:10.1021/ac0480850 · Cited by 518

Organoid-on-a-chip and body-on-a-chip systems for drug screening and disease modeling

Skardal, A., Shupe, T., & Atala, A. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.07.003 · Cited by 512

Biology‐Oriented Synthesis

Wetzel, S., Bon, R. S., Kumar, K., & Waldmann, H. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201007004 · Cited by 505

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 494

Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?

Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484

General methods for the construction of complex molecules

Corey, E. J. (1967) · Pure and Applied Chemistry · doi:10.1351/pac196714010019 · Cited by 470

Organic Synthesis: March of the Machines

Ley, S. V., Fitzpatrick, D. E., Ingham, R. J., & Myers, R. M. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201410744 · Cited by 455

Flash chemistry: flow chemistry that cannot be done in batch

Yoshida, J., Takahashi, Y., & Nagaki, A. (2013) · Chemical Communications · doi:10.1039/c3cc44709j · Cited by 454

The Medicinal Chemistry of Therapeutic Oligonucleotides

Wan, W. B., & Seth, P. P. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00551 · Cited by 445

Diversity-oriented synthesis: producing chemical tools for dissecting biology

Connor, C. J. O., Beckmann, H. S. G., & Spring, D. R. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35023h · Cited by 438

Dynamic Combinatorial Chemistry

Lehn, J., & Eliseev, A. V. (2001) · Science · doi:10.1126/science.1060066 · Cited by 403

Practical High-Throughput Experimentation for Chemists

Shevlin, M. (2017) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.7b00165 · Cited by 400

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395

A Scoring Scheme for Discriminating between Drugs and Nondrugs

Sadowski, J., & Kubinyi, H. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm9706776 · Cited by 392

Combinatorial chemistry in drug discovery

Liu, R., Li, X., & Lam, K. S. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2017.03.017 · Cited by 333

Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase

Obexer, R., Godina, A., Garrabou, X., Mittl, P. R. E., Baker, D. A., Griffiths, A. D., & Hilvert, D. (2016) · Nature Chemistry · doi:10.1038/nchem.2596 · Cited by 316

Intelligent routes to the controlled synthesis of nanoparticles

Krishnadasan, S. H., Brown, R. J. C., deMello, A. J., & deMello, J. C. (2007) · Lab on a Chip · doi:10.1039/b711412e · Cited by 310

Solid-phase peptide synthesis: an overview focused on the preparation of biologically relevant peptides

Palomo, J. M. (2014) · RSC Advances · doi:10.1039/c4ra02458c · Cited by 301

Flow Chemistry: Intelligent Processing of Gas–Liquid Transformations Using a Tube-in-Tube Reactor

Brzozowski, M., O’Brien, M., Ley, S. V., & Polyzos, A. (2015) · Accounts of Chemical Research · doi:10.1021/ar500359m · Cited by 295

DENDRAL: A case study of the first expert system for scientific hypothesis formation

Lindsay, R., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90068-m · Cited by 292

Microfluidics for cell-based high throughput screening platforms—A review

Du, G., Fang, Q., & den Toonder, J. M. J. (2016) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.11.023 · Cited by 276

Active-learning strategies in computer-assisted drug discovery

Reker, D., & Schneider, G. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.12.004 · Cited by 275

Recognizing molecules with drug-like properties

Walters, W. P., Murcko, A. A., & Murcko, M. A. (1999) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(99)80058-1 · Cited by 265

Is Multitask Deep Learning Practical for Pharma?

Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263

Feedback in Flow for Accelerated Reaction Development

Reizman, B. J., & Jensen, K. F. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00261 · Cited by 261

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsPreprint

Goh, G. B., Siegel, C., Vishnu, A., Hodas, N. O., & Baker, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06689 · Cited by 251

The Next Era: Deep Learning in Pharmaceutical Research

Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 249

Contemporary screening approaches to reaction discovery and development

Collins, K. D., Gensch, T., & Glorius, F. (2014) · Nature Chemistry · doi:10.1038/nchem.2062 · Cited by 242

Applications of micromixing technology

Jeong, G. S., Chung, S. H., Kim, C.-B., & Lee, S. (2010) · The Analyst · doi:10.1039/b921430e · Cited by 241

3D Printed Microtransporters: Compound Micromachines for Spatiotemporally Controlled Delivery of Therapeutic Agents

Huang, T., Sakar, M. S., Mao, A. S., Petruska, A. J., Qiu, F., Chen, X., Kennedy, S. M., Mooney, D. J., & Nelson, B. J. (2015) · Advanced Materials · doi:10.1002/adma.201503095 · Cited by 241

A renaissance of neural networks in drug discovery

Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker, D., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320001111 · Cited by 228

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Sun, J., Jeliazkova, N., Chupakhin, V., Dzib, J. F. G., Engkvist, O., Carlsson, L., Wegner, J. K., Ceulemans, H., Georgiev, I., Jeliazkov, V., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0203-5 · Cited by 227

An Integrated Microreactor System for Self‐Optimization of a Heck Reaction: From Micro‐ to Mesoscale Flow Systems

McMullen, J. P., Stone, M. T., Buchwald, S. L., & Jensen, K. F. (2010) · Angewandte Chemie International Edition · doi:10.1002/anie.201002590 · Cited by 222

TensorFlow: Biology’s Gateway to Deep Learning?

Rampášek, L., & Goldenberg, A. (2016) · Cell Systems · doi:10.1016/j.cels.2016.01.009 · Cited by 222

Dynamic combinatorial chemistry: a tool to facilitate the identification of inhibitors for protein targets

Mondal, M., & Hirsch, A. K. H. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00493k · Cited by 216

Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters

Li, Y., Chen, C., & Wasserman, W. W. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0189 · Cited by 215

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 212

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 202

Hydrogel Droplet Microfluidics for High-Throughput Single Molecule/Cell Analysis

Zhu, Z., & Yang, C. J. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00370 · Cited by 199

Towards Robot Scientists for autonomous scientific discovery

Sparkes, A., Aubrey, W., Byrne, E., Clare, A. J., Khan, M. N. A., Liakata, M., Markham, M., Rowland, J. J., Soldatova, L., Whelan, K. E., et al. (2010) · Automated Experimentation · doi:10.1186/1759-4499-2-1 · Cited by 195

Learning to Predict Chemical Reactions

Kayala, M. A., Azencott, C., Chen, J. H., & Baldi, P. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200207y · Cited by 194

A flow-based synthesis of Imatinib: the API of Gleevec

Hopkin, M. D., Baxendale, I. R., & Ley, S. V. (2010) · Chemical Communications · doi:10.1039/c001550d · Cited by 193

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

Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations

Zong, N., Kim, H., Ngo, V., & Harismendy, O. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx160 · Cited by 190

Molecular inflation, attrition and the rule of five

Leeson, P. D. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.018 · Cited by 184

From Synthesis to Function via Iterative Assembly of N-Methyliminodiacetic Acid Boronate Building Blocks

Li, J., Grillo, A. S., & Burke, M. D. (2015) · Accounts of Chemical Research · doi:10.1021/acs.accounts.5b00128 · Cited by 183

Boosting compound-protein interaction prediction by deep learning

Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016) · Methods · doi:10.1016/j.ymeth.2016.06.024 · Cited by 182

Chemoinformatic Classification Methods and their Applicability Domain

Mathea, M., Klingspohn, W., & Baumann, K. (2016) · Molecular Informatics · doi:10.1002/minf.201501019 · Cited by 175

Toolkits and Libraries for Deep Learning

Erickson, B. J., Korfiatis, P. D., Akkus, Z., Kline, T. L., & Philbrick, K. A. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9965-6 · Cited by 175

“Batch” Kinetics in Flow: Online IR Analysis and Continuous Control

Moore, J. S., & Jensen, K. F. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201306468 · Cited by 169

Design and synthesis of analogues of natural products

Maier, M. E. (2015) · Organic & Biomolecular Chemistry · doi:10.1039/c5ob00169b · Cited by 169

De Novo Design at the Edge of Chaos

Schneider, P., & Schneider, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01849 · Cited by 168

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

Skoraczyński, G., Dittwald, P., Miasojedow, B., Szymkuć, S., Gajewska, E. P., Grzybowski, B. A., & Gambin, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-02303-0 · Cited by 155

Continuous Flow-Processing of Organometallic Reagents Using an Advanced Peristaltic Pumping System and the Telescoped Flow Synthesis of ( E/Z )-Tamoxifen

Murray, P. R. D., Browne, D. L., Pastre, J. C., Butters, C., Guthrie, D., & Ley, S. V. (2013) · Organic Process Research & Development · doi:10.1021/op4001548 · Cited by 151

Rapid Discovery of a Novel Series of Abl Kinase Inhibitors by Application of an Integrated Microfluidic Synthesis and Screening Platform

Desai, B. K., Dixon, K., Farrant, E., Feng, Q., Gibson, K. R., van Hoorn, W. P., Mills, J. E. J., Morgan, T., Parry, D. M., Ramjee, M. K., et al. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400099d · Cited by 142

Microwave Reactions Under Continuous Flow Conditions

Baxendale, I. R., Hayward, J. J., & Ley, S. V. (2007) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620707783220374 · Cited by 141

Computer‐Assisted Planning of Organic Syntheses: The Second Generation of Programs

Ihlenfeldt, W., & Gasteiger, J. (1996) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199526131 · Cited by 138

The changing model of big pharma: impact of key trends

Gautam, A., & Pan, X. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.10.002 · Cited by 138

Considering the impact drug-like properties have on the chance of success

Yusof, I., & Segall, M. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.02.008 · Cited by 137

Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry

Kowalik, M., Gothard, C. M., Drews, A. M., Gothard, N. A., Weckiewicz, A., Fuller, P. E., Grzybowski, B. A., & Bishop, K. J. M. (2012) · Angewandte Chemie International Edition · doi:10.1002/anie.201202209 · Cited by 134

Machine learning for epigenetics and future medical applications

Holder, L. B., Haque, M. M., & Skinner, M. K. (2017) · Epigenetics · doi:10.1080/15592294.2017.1329068 · Cited by 133

Total Synthesis and Absolute Configuration of the Guaiane Sesquiterpene Englerin A

Willot, M., Radtke, L., Könning, D., Fröhlich, R., Gessner, V. H., Carsten Strohmann, B., & Christmann, M. (2009) · Angewandte Chemie International Edition · doi:10.1002/anie.200905032 · Cited by 128

Accelerating Spirocyclic Polyketide Synthesis using Flow Chemistry

Newton, S., Carter, C. F., Pearson, C. M., Alves, L. D. C., Lange, H., Thansandote, P., & Ley, S. V. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201402056 · Cited by 128

A remote-controlled adaptive medchem lab: an innovative approach to enable drug discovery in the 21st Century

Godfrey, A. G., Masquelin, T., & Hemmerle, H. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.03.001 · Cited by 127

Cancer-on-a-chip systems at the frontier of nanomedicine

Zhang, Y. S., Zhang, Y.-N., & Zhang, W. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.03.011 · Cited by 127

Privileged Structures Revisited

Schneider, P., & Schneider, G. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201702816 · Cited by 127

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Tetko, I. V., Engkvist, O., Koch, U., Reymond, J., & Chen, H. (2016) · Molecular Informatics · doi:10.1002/minf.201600073 · Cited by 125

Making a big thing of a small cell – recent advances in single cell analysis

Galler, K., Bräutigam, K., Große, C., Popp, J., & Neugebauer, U. (2014) · The Analyst · doi:10.1039/c3an01939j · Cited by 124

The use of a continuous flow-reactor employing a mixed hydrogen–liquid flow stream for the efficient reduction of imines to amines

Saaby, S., Knudsen, K. R., Ladlow, M., & Ley, S. V. (2005) · Chemical Communications · doi:10.1039/b504854k · Cited by 123

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

Winkler, D. A., & Le, T. C. (2016) · Molecular Informatics · doi:10.1002/minf.201600118 · Cited by 123

Synthesis and analysis of combinatorial libraries performed in an automated micro reactor system

García‐Egido, E., Spikmans, V., Wong, S. Y. F., & Warrington, B. H. (2003) · Lab on a Chip · doi:10.1039/b302381h · Cited by 121

Accessing Drug Metabolites via Transition‐Metal Catalyzed C−H Oxidation: The Liver as Synthetic Inspiration

Genovino, J., Sameš, D., Hamann, L. G., & Touré, B. B. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201602644 · Cited by 121

Computer‐aided synthesis design: 40 years on

Cook, A. P. F., Johnson, A. P., Law, J. B., Mirzazadeh, M., Ravitz, O., & Simon, A. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.61 · Cited by 115

Chemistry and Biology in Femtoliter and Picoliter Volume Droplets

Chiu, D. T.-Y., & Lorenz, R. M. (2009) · Accounts of Chemical Research · doi:10.1021/ar8002464 · Cited by 113

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 112

Lab automation and robotics: Automation on the move

Chapman, T. (2003) · Nature · doi:10.1038/421661a · Cited by 111

A microfluidic device for epigenomic profiling using 100 cells

Cao, Z., Chen, C., He, B., Tan, K., & Lu, C. (2015) · Nature Methods · doi:10.1038/nmeth.3488 · Cited by 111

Small-Volume Nuclear Magnetic Resonance Spectroscopy

Fratila, R. M., & Velders, A. H. (2011) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-061010-114024 · Cited by 109

An integrated microfluidic device for large-scale in situ click chemistry screening

Wang, Y., Lin, W., Liu, K., Lin, R. J., Selke, M., Kolb, H. C., Zhang, N., Zhao, X., Phelps, M. E., Shen, C. K.-F., et al. (2009) · Lab on a Chip · doi:10.1039/b907430a · Cited by 108

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 107

Rationalizing Tight Ligand Binding through Cooperative Interaction Networks

Kuhn, B., Fuchs, J. E., Reutlinger, M., Ståhl, M., & Taylor, N. R. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200319e · Cited by 106

Development of Droplet Microfluidics Enabling High-Throughput Single-Cell Analysis

Wen, N., Zhao, Z., Fan, B., Chen, D., Men, D., Wang, J., & Chen, J. (2016) · Molecules · doi:10.3390/molecules21070881 · Cited by 105

WAT-on-a-chip: a physiologically relevant microfluidic system incorporating white adipose tissue

Loskill, P., Thiagarajan, S., Tharp, K. M., Lee-Montiel, F. T., Jeeawoody, S., Reese, W. M., Zushin, P. H., Stahl, A., & Healy, K. E. (2017) · Lab on a Chip · doi:10.1039/c6lc01590e · Cited by 103

Droplet-Based Microfluidics: Enabling Impact on Drug Discovery

Dressler, O. J., Maceiczyk, R. M., Chang, S., & deMello, A. J. (2013) · SLAS DISCOVERY · doi:10.1177/1087057113510401 · Cited by 98

Organic synthesis: The robo-chemist

Peplow, M. A. (2014) · Nature · doi:10.1038/512020a · Cited by 97

Accessing New Chemical Entities through Microfluidic Systems

Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201400988 · Cited by 94

The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space

Nicolaou, C. A., Watson, I. A., Hu, H., & Wang, J. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00173 · Cited by 94

An Integrated Microfluidic Processor for DNA-Encoded Combinatorial Library Functional Screening

MacConnell, A. B., Price, A. K., & Paegel, B. M. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.6b00192 · Cited by 93

Preparative Microfluidic Electrosynthesis of Drug Metabolites

Stalder, R., & Roth, G. P. (2013) · ACS Medicinal Chemistry Letters · doi:10.1021/ml400316p · Cited by 91

Enabling future drug discovery by de novo design

Hartenfeller, M., & Schneider, G. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.49 · Cited by 89

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 89

Implementing Enzyme-Linked Immunosorbent Assays on a Microfluidic Chip To Quantify Intracellular Molecules in Single Cells

Eyer, K., Stratz, S., Kuhn, P., Küster, S. K., & Dittrich, P. S. (2013) · Analytical Chemistry · doi:10.1021/ac303628j · Cited by 88

Moving Liquids with Sound: The Physics of Acoustic Droplet Ejection for Robust Laboratory Automation in Life Sciences

Hadimioglu, B. B., Stearns, R. G., & Ellson, R. N. (2015) · SLAS TECHNOLOGY · doi:10.1177/2211068215615096 · Cited by 88

Recent lab‐on‐chip developments for novel drug discovery

Khalid, N., Kobayashi, I., & Nakajima, M. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1381 · Cited by 86

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 85

Continuous flow synthesis

Yoshida, J., Nagaki, A., & Yamada, D. (2012) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2012.10.013 · Cited by 85

No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms

Chen, J. H., & Baldi, P. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900157k · Cited by 85

DNA Bipedal Motor Achieves a Large Number of Steps Due to Operation Using Microfluidics-Based Interface

Tomov, T. E., Tsukanov, R., Glick, Y., Berger, Y., Liber, M., Avrahami, D., Gerber, D., & Nir, E. (2017) · ACS Nano · doi:10.1021/acsnano.7b00547 · Cited by 85

Optimizing the Heck–Matsuda Reaction in Flow with a Constraint-Adapted Direct Search Algorithm

Cortés‐Borda, D., Kutonova, K., Jamet, C., Trusova, M. E., Zammattio, F., Truchet, C., Rodriguez‐Zubiri, M., & Felpin, F. (2016) · Organic Process Research & Development · doi:10.1021/acs.oprd.6b00310 · Cited by 83

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

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

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 79

Peptide design by artificial neural networks and computer-based evolutionary search

Schneider, G., Schrödl, W., Wallukat, G., Müller, J., Nissen, E., Rönspeck, W., Wrede, P., & Kunze, R. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.21.12179 · Cited by 75

De novo Molecular Design

Schneider, G. (2013) · Wiley eBooks · doi:10.1002/9783527677016 · Cited by 75

Function through Synthesis-Informed Design

Wender, P. A., Quiroz, R. V., & Stevens, M. C. (2015) · Accounts of Chemical Research · doi:10.1021/acs.accounts.5b00004 · Cited by 74

Deep learning for single-molecule science

Albrecht, T., Slabaugh, G., Alonso, E., & Al-Arif, S. M. M. R. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa8334 · Cited by 74

Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

Reutlinger, M., & Schneider, G. (2012) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2011.12.006 · Cited by 72

Combining On‐Chip Synthesis of a Focused Combinatorial Library with Computational Target Prediction Reveals Imidazopyridine GPCR Ligands

Reutlinger, M., Rodrigues, T., Schneider, P., & Schneider, G. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201307786 · Cited by 68

Integrated Synthesis and Testing of Substituted Xanthine Based DPP4 Inhibitors: Application to Drug Discovery

Czechtizky, W., Dedio, J., Desai, B. K., Dixon, K., Farrant, E., Feng, Q., Morgan, T., Parry, D. M., Ramjee, M. K., Selway, C. N., et al. (2013) · ACS Medicinal Chemistry Letters · doi:10.1021/ml400171b · Cited by 65

Synthesis of a three-member array of cycloadducts in a glass microchip under pressure driven flow

Fernandez‐Suarez, M., Wong, S. Y. F., & Warrington, B. H. (2002) · Lab on a Chip · doi:10.1039/b202324e · Cited by 61

Benchmarking a Wide Range of Chemical Descriptors for Drug‐Target Interaction Prediction Using a Chemogenomic Approach

Sawada, R., Kotera, M., & Yamanishi, Y. (2014) · Molecular Informatics · doi:10.1002/minf.201400066 · Cited by 61

Molecular Diversity and Combinatorial Chemistry

Banfi, L. (2006) · Molecular Diversity · doi:10.1007/s11030-006-2894-9 · Cited by 60

Harnessing Big Data for Systems Pharmacology

Xie, L., Draizen, E. J., & Bourne, P. E. (2016) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010716-104659 · Cited by 58

Principles, implementation, and application of biology-oriented synthesis (BIOS)

Wilk, W., Zimmermann, T. J., Kaiser, M., & Waldmann, H. (2009) · Biological Chemistry · doi:10.1515/bc.2010.013 · Cited by 56

3D Droplet Microfluidic Systems for High-Throughput Biological Experimentation

Kang, D., Gong, X., Cho, S., Kim, J., Edel, J. B., Chang, S., Choo, J., & deMello, A. J. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b02402 · Cited by 55

ChemInform Abstract: Profound Methyl Effects in Drug Discovery and a Call for New C—H Methylation Reactions

Schoenherr, H., & Cernak, T. (2014) · ChemInform · doi:10.1002/chin.201405245 · Cited by 55

Advances in microfluidics for drug discovery

Lombardi, D., & Dittrich, P. S. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.521149 · Cited by 54

Seamless Integration of Dose‐Response Screening and Flow Chemistry: Efficient Generation of Structure–Activity Relationship Data of β‐Secretase (BACE1) Inhibitors

Werner, M., Kuratli, C., Martin, R. E., Hochstrasser, R., Wechsler, D., Enderle, T., Alanine, A. I., & Vogel, H. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201309301 · Cited by 54

A Computational Method for Unveiling the Target Promiscuity of Pharmacologically Active Compounds

Schneider, P., & Schneider, G. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201706376 · Cited by 52

Feasibility of Active Machine Learning for Multiclass Compound Classification

Lang, T., Flachsenberg, F., Ulrike Von Luxburg, A., & Rarey, M. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00332 · Cited by 50

New Avenues to Efficient Chemical Synthesis

Seeberger, P. H., & Blume, T. (2007) · Ernst Schering Research Foundation workshop/Ernst Schering Foundation Symposium proceedings · doi:10.1007/978-3-540-70849-0 · Cited by 50

Smart DNA Fabrication Using Sound Waves: Applying Acoustic Dispensing Technologies to Synthetic Biology

Kanigowska, P., Shen, Y., Zheng, Y., Rosser, S. J., & Cai, Y. (2015) · SLAS TECHNOLOGY · doi:10.1177/2211068215593754 · Cited by 49

Strong Nonadditivity as a Key Structure–Activity Relationship Feature: Distinguishing Structural Changes from Assay Artifacts

Krämer, C., Fuchs, J. E., & Liedl, K. R. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00018 · Cited by 49

Neural networks are useful tools for drug design

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

Enhanced HTS Hit Selection via a Local Hit Rate Analysis

Posner, B. A., Xi, H. S., & Mills, J. E. J. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900113d · Cited by 48

From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design

Friedrich, L., Rodrigues, T., Neuhaus, C. S., Schneider, P., & Schneider, G. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201601941 · Cited by 48

Process Intensified Flow Synthesis of 1H-4-Substituted Imidazoles: Toward the Continuous Production of Daclatasvir

Carneiro, P. F., Gutmann, B., de Souza, R. O. M. A., & Kappe, C. O. (2015) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5b01191 · Cited by 47

A droplet-chip/mass spectrometry approach to study organic synthesis at nanoliter scale

Beulig, R. J., Warias, R., Heiland, J. J., Ohla, S., Zeitler, K., & Belder, D. (2017) · Lab on a Chip · doi:10.1039/c7lc00313g · Cited by 47

Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Miyao, T., Arakawa, M., & Funatsu, K. (2010) · Molecular Informatics · doi:10.1002/minf.200900038 · Cited by 46

Measuring the effectiveness and impact of an open innovation platform

Carroll, G. P., Srivastava, S., Volini, A. S., Piñeiro-Núñez, M. M., & Vetman, T. N. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.01.009 · Cited by 46

An Integrated Synthesis-Purification System to Accelerate the Generation of Compounds in Pharmaceutical Discovery

Hochlowski, J. E., Searle, P. A., Tu, N. P., Pan, J., Spanton, S. G., & Djurić, S. W. (2011) · Journal of Flow Chemistry · doi:10.1556/jfchem.2011.00013 · Cited by 44

Data-driven computer aided synthesis design

Ravitz, O. (2013) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2013.01.005 · Cited by 44

Technologies That Enable Accurate and Precise Nano- to Milliliter-Scale Liquid Dispensing of Aqueous Reagents Using Acoustic Droplet Ejection

Sackmann, E. K., Majlof, L., Hahn-Windgassen, A., Eaton, B., Bandzava, T., Daulton, J., Vandenbroucke, A., Mock, M., Stearns, R. G., Hinkson, S., et al. (2015) · SLAS TECHNOLOGY · doi:10.1177/2211068215602191 · Cited by 43

Artificial neural networks and simulated molecular evolution are potential tools for sequence-oriented protein design

Schneider, G., Schuchhardt, J., & Wrede, P. (1994) · Computer applications in the biosciences · doi:10.1093/bioinformatics/10.6.635 · Cited by 43

Medicinal Chemistry for 2020

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

3D printed fluidics with embedded analytic functionality for automated reaction optimisation

Capel, A. J., Wright, A. D., Harding, M. J., Weaver, G. W., Li, Y., Harris, R. A., Edmondson, S., Goodridge, R., & Christie, S. D. R. (2017) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.13.14 · Cited by 43

Total Synthesis of (±)‐Englerin A Using An Intermolecular [3+2] Cycloaddition Reaction of Platinum‐Containing Carbonyl Ylide

Kusama, H., Tazawa, A., Ishida, K., & Iwasawa, N. (2015) · Chemistry - An Asian Journal · doi:10.1002/asia.201500935 · Cited by 42

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

Automated medicinal chemistry

Koppitz, M., & Eis, K. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.04.005 · Cited by 40

Neighborhood‐Preserving Visualization of Adaptive Structure–Activity Landscapes: Application to Drug Discovery

Reutlinger, M., Guba, W., Martin, R. E., Alanine, A. I., Hoffmann, T., Klenner, A., Hiss, J. A., Schneider, P., & Schneider, G. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201105156 · Cited by 40

Microfluidic platforms for DNA methylation analysis

Kurita, R., & Niwa, O. (2016) · Lab on a Chip · doi:10.1039/c6lc00829a · Cited by 40

Direct Surface and Droplet Microsampling for Electrospray Ionization Mass Spectrometry Analysis with an Integrated Dual-Probe Microfluidic Chip

Huang, C.-M., Zhu, Y., Jin, D., Kelly, R., & Fang, Q. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b01679 · Cited by 38

Robots with instincts

Adami, C. (2015) · Nature · doi:10.1038/521426a · Cited by 37

Leveraging the Advantages Offered by Microfluidics to Enhance the Drug Discovery Process

Hawkes, S. Y. F. W., Chapela, M. J. V., & Montembault, M. (2005) · QSAR & Combinatorial Science · doi:10.1002/qsar.200440001 · Cited by 37

Flash synthesis of TAC-101 and its analogues from 1,3,5-tribromobenzene using integrated flow microreactor systems

Nagaki, A., Imai, K., Kim, H., & Yoshida, J. (2011) · RSC Advances · doi:10.1039/c1ra00377a · Cited by 37

Spotting and designing promiscuous ligands for drug discovery

Schneider, P., Röthlisberger, M., Reker, D., & Schneider, G. (2015) · Chemical Communications · doi:10.1039/c5cc07506h · Cited by 37

Stargate GTM: Bridging Descriptor and Activity Spaces

Gaspar, H. A., Baskin, I. I., Marcou, G., Horvath, D., & Varnek, A. A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00398 · Cited by 36

Rapid Access to Compound Libraries through Flow Technology: Fully Automated Synthesis of a 3-Aminoindolizine Library via Orthogonal Diversification

Lange, P. P. M. D., & James, K. (2012) · ACS Combinatorial Science · doi:10.1021/co300094n · Cited by 36

Continuous processes for the production of pharmaceutical intermediates and active pharmaceutical ingredients.

LaPorte, T. L., & Wang, C. (2007) · PubMed · Cited by 35

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

De Novo Fragment Design for Drug Discovery and Chemical Biology

Rodrigues, T., Reker, D., Welin, M., Caldera, M., Brunner, C., Gabernet, G., Schneider, P., Walse, B., & Schneider, G. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201508055 · Cited by 33

Simplified Mesofluidic Systems for the Formation of Micron to Millimeter Droplets and the Synthesis of Materials

Steinbacher, J. L., Lui, Y., Mason, B. P., Olbricht, W. L., & McQuade, D. T. (2012) · Journal of Flow Chemistry · doi:10.1556/jfc-d-11-00023 · Cited by 33

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

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

Thomford, N. E., Senthebane, D. A., Rowe, A., Munro, D., Seele, P. P., Maroyi, A., & Dzobo, K. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061578 · Cited by 1,301

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180

A robotic platform for flow synthesis of organic compounds informed by AI planning

Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V. L., Hart, T., Fishman, J., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,133

ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

Kalayil, N., D'Souza, S. S., Khan, S., & PAUL, P. (2022) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2022.v15i4.43890 · Cited by 9

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Gioiello, A., Piccinno, A., Lozza, A. M., & Cerra, B. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01956 · Cited by 153

Is combinatorial chemistry on the right track for drug discovery? †

Wildonger, R. A., Deegan, T. L., & Lee, J. W. (2003) · Journal of Automated Methods and Management in Chemistry · doi:10.1080/1463924032000121110 · Cited by 4

AI-driven automated chemistry as a tool to accelerate drug discovery

Simeonov, A. M., & Zakharov, A. V. (2019)

Is combinatorial chemistry on the right track for drug discovery?y

Wildonger, R. A., Deegan, T. L., & Lee, J. W. (2013)