Automating drug discovery

Deep learning

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

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Can we open the black box of AI?

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

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Robots that can adapt like animals

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Computer-based de novo design of drug-like molecules

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Protein–Ligand Scoring with Convolutional Neural Networks

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Low Data Drug Discovery with One-Shot Learning

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Prediction of Organic Reaction Outcomes Using Machine Learning

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Deep Learning in Drug Discovery

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Applications of Deep Learning in Biomedicine

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The automation of science

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Flow chemistry syntheses of natural products

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

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Flash Chemistry: Fast Chemical Synthesis by Using Microreactors

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

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Predicting drug metabolism: experiment and/or computation?

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Generative Recurrent Networks for De Novo Drug Design

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Machine learning for epigenetics and future medical applications

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Total Synthesis and Absolute Configuration of the Guaiane Sesquiterpene Englerin A

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Accelerating Spirocyclic Polyketide Synthesis using Flow Chemistry

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

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Privileged Structures Revisited

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

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BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

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

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

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

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

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

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Computer‐aided synthesis design: 40 years on

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Chemistry and Biology in Femtoliter and Picoliter Volume Droplets

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

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Lab automation and robotics: Automation on the move

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A microfluidic device for epigenomic profiling using 100 cells

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Small-Volume Nuclear Magnetic Resonance Spectroscopy

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

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Rationalizing Tight Ligand Binding through Cooperative Interaction Networks

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

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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

Droplet-Based Microfluidics: Enabling Impact on Drug Discovery

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

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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 · see citing works

Organic synthesis: The robo-chemist

Peplow, M. A. (2014) · Nature · doi:10.1038/512020a · Cited by 94 · see citing works

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

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

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Preparative Microfluidic Electrosynthesis of Drug Metabolites

Stalder, R., & Roth, G. P. (2013) · ACS Medicinal Chemistry Letters · doi:10.1021/ml400316p · Cited by 92 · 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

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

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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 · see citing works

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

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Continuous flow synthesis

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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 84 · see citing works

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 84 · 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

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

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Recent lab‐on‐chip developments for novel drug discovery

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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

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

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

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Function through Synthesis-Informed Design

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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 · see citing works

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 72 · see citing works

De novo Molecular Design

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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 69 · see citing works

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 · see citing works

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

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Molecular Diversity and Combinatorial Chemistry

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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 58 · see citing works

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 57 · see citing works

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

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3D Droplet Microfluidic Systems for High-Throughput Biological Experimentation

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

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Advances in microfluidics for drug discovery

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

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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 51 · see citing works

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 · see citing works

New Avenues to Efficient Chemical Synthesis

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

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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

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 · see citing works

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 48 · see citing works

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 · see citing works

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 · see citing works

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

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Exhaustive Structure Generation for Inverse‐QSPR/QSAR

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Data-driven computer aided synthesis design

Ravitz, O. (2013) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2013.01.005 · Cited by 45 · see citing works

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

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

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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 · 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

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

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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 · 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

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 41 · see citing works

Automated medicinal chemistry

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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 · see citing works

Microfluidic platforms for DNA methylation analysis

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

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 · see citing works

Robots with instincts

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

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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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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

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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

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 · see citing works

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 · see citing works

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

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On the Opportunities and Risks of Foundation Models

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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 1,268 · see citing works

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

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

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Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works

Rethinking drug design in the artificial intelligence era

Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works

QSAR without borders

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