| scholarly article | Q13442814 |
| P356 | DOI | 10.1038/NRD.2017.232 |
| P698 | PubMed publication ID | 29242609 |
| P50 | author | Gisbert Schneider | Q51615601 |
| P2860 | cites work | Towards Robot Scientists for autonomous scientific discovery. | Q21198755 |
| Ultrahigh-throughput screening in drop-based microfluidics for directed evolution | Q24626072 | ||
| Development of Droplet Microfluidics Enabling High-Throughput Single-Cell Analysis | Q26741345 | ||
| Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data | Q27301054 | ||
| Smart DNA Fabrication Using Sound Waves: Applying Acoustic Dispensing Technologies to Synthetic Biology | Q27321924 | ||
| Microfluidics: Fluid physics at the nanoliter scale | Q27349505 | ||
| De Novo Fragment Design for Drug Discovery and Chemical Biology | Q27702495 | ||
| Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings | Q27861111 | ||
| The automation of science. | Q27937312 | ||
| Deep learning | Q28018765 | ||
| Current status and future prospects for enabling chemistry technology in the drug discovery process | Q28066266 | ||
| Exhaustive Structure Generation for Inverse-QSPR/QSAR. | Q51758769 | ||
| Artificial neural networks for computer-based molecular design | Q52230724 | ||
| A Scoring Scheme for Discriminating between Drugs and Nondrugs | Q52237197 | ||
| Artificial neural networks and simulated molecular evolution are potential tools for sequence-oriented protein design | Q52365048 | ||
| Embarking on a Chemical Space Odyssey. | Q52810739 | ||
| Synthesis of new hydrophilic rhodamine based enzymatic substrates compatible with droplet-based microfluidic assays | Q53319534 | ||
| Enabling Chemistry Technologies and Parallel Synthesis-Accelerators of Drug Discovery Programmes | Q53425209 | ||
| Drug Discovery Goes Three-Dimensional: Goodbye to Flat High-Throughput Screening? | Q53638950 | ||
| Generative Models for Chemical Structures | Q54659676 | ||
| Solid Phase Peptide Synthesis. I. The Synthesis of a Tetrapeptide | Q55869789 | ||
| General methods for the construction of complex molecules | Q56657673 | ||
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| Continuous Flow-Processing of Organometallic Reagents Using an Advanced Peristaltic Pumping System and the Telescoped Flow Synthesis of (E/Z)-Tamoxifen | Q56951697 | ||
| The use of a continuous flow-reactor employing a mixed hydrogen–liquid flow stream for the efficient reduction of imines to amines | Q56952562 | ||
| Synthesis of Cesium Lead Halide Perovskite Nanocrystals in a Droplet-Based Microfluidic Platform: Fast Parametric Space Mapping | Q59389387 | ||
| Solid-phase peptide synthesis: an overview focused on the preparation of biologically relevant peptides | Q60530895 | ||
| Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors | Q61218037 | ||
| Spotting and designing promiscuous ligands for drug discovery | Q61218042 | ||
| Multidimensional De Novo Design Reveals 5-HT2BReceptor-Selective Ligands | Q61218051 | ||
| Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry | Q61311517 | ||
| From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design | Q62608586 | ||
| Neighborhood-Preserving Visualization of Adaptive Structure-Activity Landscapes: Application to Drug Discovery | Q62763053 | ||
| Dynamic combinatorial chemistry | Q73673756 | ||
| Automated flow-through synthesis of heterocyclic thioethers | Q80314513 | ||
| Microreactors as new tools for drug discovery and development | Q80789911 | ||
| Flash chemistry: flow chemistry that cannot be done in batch | Q85877555 | ||
| Technologies That Enable Accurate and Precise Nano- to Milliliter-Scale Liquid Dispensing of Aqueous Reagents Using Acoustic Droplet Ejection | Q86074724 | ||
| Total Synthesis of (±)-Englerin A Using An Intermolecular [3+2] Cycloaddition Reaction of Platinum-Containing Carbonyl Ylide | Q86175179 | ||
| Moving Liquids with Sound: The Physics of Acoustic Droplet Ejection for Robust Laboratory Automation in Life Sciences | Q86628859 | ||
| March of the synthesis machines | Q87212793 | ||
| Structure Modification toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property | Q47786258 | ||
| Reaction screening and optimization of continuous-flow atropine synthesis by preparative electrospray mass spectrometry | Q47929470 | ||
| Direct Surface and Droplet Microsampling for Electrospray Ionization Mass Spectrometry Analysis with an Integrated Dual-Probe Microfluidic Chip. | Q48285407 | ||
| Modeling the Blood-Brain Barrier in a 3D triple co-culture microfluidic system | Q49043392 | ||
| Can we open the black box of AI? | Q50485085 | ||
| TensorFlow: Biology's Gateway to Deep Learning? | Q50516290 | ||
| Stargate GTM: Bridging Descriptor and Activity Spaces | Q50555989 | ||
| Function-Oriented Synthesis: How to Design Simplified Analogues of Antibacterial Nucleoside Natural Products? | Q50865707 | ||
| A droplet-chip/mass spectrometry approach to study organic synthesis at nanoliter scale. | Q50871545 | ||
| Artificial intelligence: Robots with instincts | Q50907866 | ||
| De novo Drug Design - Ye olde Scoring Problem Revisited | Q51028429 | ||
| Organic synthesis: The robo-chemist | Q51060824 | ||
| Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase | Q51071757 | ||
| A remote-controlled adaptive medchem lab: an innovative approach to enable drug discovery in the 21st Century | Q51241120 | ||
| Where will we get the next generation of medicinal chemists? | Q51529193 | ||
| Implementing Enzyme-Linked Immunosorbent Assays on a Microfluidic Chip To Quantify Intracellular Molecules in Single Cells | Q51533618 | ||
| Intelligent routes to the controlled synthesis of nanoparticles | Q51577482 | ||
| Synthesis of a three-member array of cycloadducts in a glass microchip under pressure driven flow | Q51597180 | ||
| 3D Droplet Microfluidic Systems for High-Throughput Biological Experimentation | Q51692911 | ||
| The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space | Q51709586 | ||
| Flow chemistry syntheses of natural products | Q38133744 | ||
| Continuous flow synthesis | Q38139520 | ||
| Automation of decision making in drug design | Q38158007 | ||
| Droplet-based microfluidics: enabling impact on drug discovery | Q38163391 | ||
| Making a big thing of a small cell--recent advances in single cell analysis | Q38185233 | ||
| Accessing new chemical entities through microfluidic systems | Q38209736 | ||
| Contemporary screening approaches to reaction discovery and development | Q38253009 | ||
| The Medicinal Chemistry of Therapeutic Oligonucleotides | Q38291625 | ||
| Organic synthesis: march of the machines | Q38317728 | ||
| Flow chemistry: intelligent processing of gas-liquid transformations using a tube-in-tube reactor | Q38328281 | ||
| Design and synthesis of analogues of natural products | Q38398596 | ||
| Is Multitask Deep Learning Practical for Pharma? | Q38429996 | ||
| Predicting drug metabolism: experiment and/or computation? | Q38439385 | ||
| Why and how have drug discovery strategies in pharma changed? What are the new mindsets? | Q38587946 | ||
| A survey on deep learning in medical image analysis | Q38646751 | ||
| Deep learning for single-molecule science. | Q38649945 | ||
| A Computational Method for Unveiling the Target Promiscuity of Pharmacologically Active Compounds | Q38658125 | ||
| Microfluidics for cell-based high throughput screening platforms - A review | Q38680907 | ||
| Machine learning for epigenetics and future medical applications | Q38681800 | ||
| An integrated chemical biology approach reveals the mechanism of action of HIV replication inhibitors | Q38690962 | ||
| Learning to predict chemical reactions | Q38711501 | ||
| Molecular inflation, attrition and the rule of five | Q38719776 | ||
| WAT-on-a-chip: a physiologically relevant microfluidic system incorporating white adipose tissue. | Q38729007 | ||
| Extending 'predict first' to the design-make-test cycle in small-molecule drug discovery | Q38731318 | ||
| DNA Bipedal Motor Achieves a Large Number of Steps Due to Operation Using Microfluidics-Based Interface | Q38731326 | ||
| Cancer-on-a-chip systems at the frontier of nanomedicine | Q38733082 | ||
| Active learning for computational chemogenomics | Q38750215 | ||
| Measuring the effectiveness and impact of an open innovation platform | Q38764220 | ||
| Applications of Deep Learning in Biomedicine | Q38786217 | ||
| The Next Era: Deep Learning in Pharmaceutical Research | Q38823933 | ||
| Feedback in Flow for Accelerated Reaction Development | Q38831278 | ||
| A renaissance of neural networks in drug discovery. | Q38854003 | ||
| Protein-Ligand Scoring with Convolutional Neural Networks. | Q38860867 | ||
| Organs-on-chips at the frontiers of drug discovery | Q28083671 | ||
| Neural networks: A new method for solving chemical problems or just a passing phase? | Q28096282 | ||
| Target-oriented and diversity-oriented organic synthesis in drug discovery | Q28138837 | ||
| Automated medicinal chemistry | Q28241476 | ||
| BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry | Q28591396 | ||
| Applications of chemogenomic library screening in drug discovery | Q28656335 | ||
| Harnessing Big Data for Systems Pharmacology | Q28969528 | ||
| DENDRAL: A case study of the first expert system for scientific hypothesis formation | Q29387651 | ||
| ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics | Q30066304 | ||
| Privileged Structures Revisited | Q30101030 | ||
| Representing high throughput expression profiles via perturbation barcodes reveals compound targets | Q30362540 | ||
| Microstructured reactors as tools for the intensification of pharmaceutical reactions and processes. | Q30382329 | ||
| Computer-Assisted Synthetic Planning: The End of the Beginning. | Q30386672 | ||
| Deep learning for computational chemistry | Q30399624 | ||
| Miniaturized GPCR signaling studies in 1536-well format. | Q30493004 | ||
| Automated design of ligands to polypharmacological profiles. | Q30539786 | ||
| Do Medicinal Chemists Learn from Activity Cliffs? A Systematic Evaluation of Cliff Progression in Evolving Compound Data Sets | Q30606859 | ||
| Data-driven computer aided synthesis design | Q30667803 | ||
| Seamless integration of dose-response screening and flow chemistry: efficient generation of structure-activity relationship data of β-secretase (BACE1) inhibitors | Q30741444 | ||
| Recognizing molecules with drug-like properties | Q30741925 | ||
| Function through synthesis-informed design | Q30904733 | ||
| Semantic inference using chemogenomics data for drug discovery | Q31020503 | ||
| ADAAPT: Amgen's data access, analysis, and prediction tools | Q31052629 | ||
| Does 'Big Data' exist in medicinal chemistry, and if so, how can it be harnessed? | Q31130737 | ||
| Selective encapsulation of single cells and subcellular organelles into picoliter- and femtoliter-volume droplets | Q31153025 | ||
| Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities. | Q31159329 | ||
| Peptide design by artificial neural networks and computer-based evolutionary search | Q32000547 | ||
| Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules? | Q32032942 | ||
| Synthesis and analysis of combinatorial libraries performed in an automated micro reactor system | Q33202069 | ||
| Identification of hits and lead structure candidates with limited resources by adaptive optimization | Q33347794 | ||
| Distilling free-form natural laws from experimental data | Q33426258 | ||
| No electron left behind: a rule-based expert system to predict chemical reactions and reaction mechanisms | Q33498586 | ||
| Principles, implementation, and application of biology-oriented synthesis (BIOS). | Q33520510 | ||
| Microdroplets in microfluidics: an evolving platform for discoveries in chemistry and biology | Q33613844 | ||
| Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? | Q33804030 | ||
| Combinatorial chemistry in drug discovery | Q36369338 | ||
| From synthesis to function via iterative assembly of N-methyliminodiacetic acid boronate building blocks | Q36396226 | ||
| Lab-on-a-chip: microfluidics in drug discovery | Q36414511 | ||
| Representation of probabilistic scientific knowledge | Q36786073 | ||
| Synthesis and medical applications of oligosaccharides | Q36802338 | ||
| Microfluidic platforms for lab-on-a-chip applications | Q36916679 | ||
| Continuous processes for the production of pharmaceutical intermediates and active pharmaceutical ingredients | Q36995300 | ||
| Function-oriented synthesis, step economy, and drug design | Q37045266 | ||
| Towards a Multifunctional Electrochemical Sensing and Niosome Generation Lab-on-Chip Platform Based on a Plug-and-Play Concept | Q37068461 | ||
| Microwave reactions under continuous flow conditions | Q37089933 | ||
| Chemistry and biology in femtoliter and picoliter volume droplets | Q37198261 | ||
| Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus | Q37659834 | ||
| Applications of micromixing technology | Q37696328 | ||
| Small-volume nuclear magnetic resonance spectroscopy | Q37851494 | ||
| Deciding whether to go with the flow: evaluating the merits of flow reactors for synthesis | Q37895222 | ||
| Advances in microfluidics for drug discovery | Q38029121 | ||
| Considering the impact drug-like properties have on the chance of success | Q38086371 | ||
| Organoid-on-a-chip and body-on-a-chip systems for drug screening and disease modeling | Q38898338 | ||
| Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach | Q38916721 | ||
| Deep Learning in Drug Discovery. | Q38918718 | ||
| Chemoinformatic Classification Methods and their Applicability Domain | Q38918853 | ||
| Microfluidic platforms for DNA methylation analysis | Q38931154 | ||
| Accessing Drug Metabolites via Transition-Metal Catalyzed C-H Oxidation: The Liver as Synthetic Inspiration. | Q38977018 | ||
| Hydrogel Droplet Microfluidics for High-Throughput Single Molecule/Cell Analysis. | Q39061138 | ||
| Boosting Docking-Based Virtual Screening with Deep Learning | Q39064005 | ||
| Multi-step continuous-flow synthesis | Q39096340 | ||
| Recent lab-on-chip developments for novel drug discovery | Q39140639 | ||
| Combinatorial chemistry by ant colony optimization. | Q39243560 | ||
| Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR. | Q39247822 | ||
| Boosting compound-protein interaction prediction by deep learning | Q39631693 | ||
| Integrated Synthesis and Testing of Substituted Xanthine Based DPP4 Inhibitors: Application to Drug Discovery | Q39771545 | ||
| Preparative microfluidic electrosynthesis of drug metabolites | Q39778009 | ||
| Electrochemical imaging for microfluidics: a full-system approach | Q39971169 | ||
| Rationalizing Tight Ligand Binding through Cooperative Interaction Networks | Q39999558 | ||
| Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x). | Q40052543 | ||
| Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters | Q40073822 | ||
| Feasibility of Active Machine Learning for Multiclass Compound Classification | Q40134572 | ||
| The changing model of big pharma: impact of key trends | Q40419724 | ||
| Intelligent software for laboratory automation | Q40486918 | ||
| Lab automation and robotics: Automation on the move | Q40672514 | ||
| An eco-compatible strategy for the diversity-oriented synthesis of macrocycles exploiting carbohydrate-derived building blocks | Q41070221 | ||
| Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts | Q41245616 | ||
| Synthesis of many different types of organic small molecules using one automated process. | Q41889002 | ||
| Robots that can adapt like animals. | Q41954398 | ||
| Practical High-Throughput Experimentation for Chemists | Q42208730 | ||
| Prediction of Organic Reaction Outcomes Using Machine Learning | Q42243554 | ||
| Quantifying the chemical beauty of drugs. | Q42414958 | ||
| Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. | Q42712714 | ||
| An integrated microreactor system for self-optimization of a Heck reaction: from micro- to mesoscale flow systems | Q42931451 | ||
| A flow-based synthesis of imatinib: the API of Gleevec | Q43121529 | ||
| Total synthesis and absolute configuration of the guaiane sesquiterpene englerin A. | Q43250698 | ||
| Embedding sustainable practices into pharmaceutical R&D: what are the challenges? | Q43324729 | ||
| Lessons from 60 years of pharmaceutical innovation | Q43447062 | ||
| Enhanced HTS hit selection via a local hit rate analysis | Q43869061 | ||
| Steering target selectivity and potency by fragment-based de novo drug design | Q44053377 | ||
| From machine learning to deep learning: progress in machine intelligence for rational drug discovery. | Q45945808 | ||
| Multi-objective molecular de novo design by adaptive fragment prioritization. | Q45957534 | ||
| "Batch" kinetics in flow: online IR analysis and continuous control | Q46061513 | ||
| Development of a Specific Substrate-Inhibitor Panel (Liver-on-a-Chip) for Evaluation of Cytochrome P450 Activity | Q46455202 | ||
| The influence of drug-like concepts on decision-making in medicinal chemistry | Q46922861 | ||
| On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system | Q47446315 | ||
| Corrigendum: Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem Activity Cliffs, and QSAR. | Q47616171 | ||
| Flash chemistry: fast chemical synthesis by using microreactors | Q47657172 | ||
| An integrated microfluidic device for large-scale in situ click chemistry screening | Q33883864 | ||
| Neural networks are useful tools for drug design | Q33997819 | ||
| Biology-oriented synthesis | Q34061956 | ||
| Overview of Combinatorial Chemistry | Q34147222 | ||
| Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery | Q34156744 | ||
| The medicinal chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates. | Q34179351 | ||
| Diversity-oriented synthesis: producing chemical tools for dissecting biology | Q34227440 | ||
| Diagnosing the decline in pharmaceutical R&D efficiency | Q34257771 | ||
| Multi-objective optimization methods in drug design. | Q34372247 | ||
| Parallel Chemistry in the 21st Century | Q34403282 | ||
| Rapid Access to Compound Libraries through Flow Technology: Fully Automated Synthesis of a 3-Aminoindolizine Library via Orthogonal Diversification | Q34407140 | ||
| Large-scale de novo DNA synthesis: technologies and applications | Q34417984 | ||
| Computer-based de novo design of drug-like molecules | Q34438895 | ||
| Dynamic combinatorial chemistry: a tool to facilitate the identification of inhibitors for protein targets. | Q34464112 | ||
| Profound methyl effects in drug discovery and a call for new C-H methylation reactions | Q35024529 | ||
| Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands | Q35054899 | ||
| Distant polypharmacology among MLP chemical probes | Q35388534 | ||
| Active-learning strategies in computer-assisted drug discovery | Q35513071 | ||
| Physicochemical Effects in the Representation of Molecular Structures for Drug Designing | Q35552137 | ||
| Medicinal Chemistry for 2020 | Q35593223 | ||
| 3D Printed Microtransporters: Compound Micromachines for Spatiotemporally Controlled Delivery of Therapeutic Agents | Q35790576 | ||
| Analysis of Past and Present Synthetic Methodologies on Medicinal Chemistry: Where Have All the New Reactions Gone? | Q35842174 | ||
| The Pictet-Spengler Reaction Still on Stage | Q35882251 | ||
| De Novo Design at the Edge of Chaos | Q35924943 | ||
| A microfluidic device for epigenomic profiling using 100 cells. | Q36108966 | ||
| No Denying It: Medicinal Chemistry Training Is in Big Trouble | Q36144673 | ||
| Macromolecular target prediction by self-organizing feature maps | Q36229691 | ||
| An Integrated Microfluidic Processor for DNA-Encoded Combinatorial Library Functional Screening | Q36281478 | ||
| 3D printed fluidics with embedded analytic functionality for automated reaction optimisation | Q36287952 | ||
| Toolkits and Libraries for Deep Learning | Q36313543 | ||
| Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations | Q36353609 | ||
| Integrated Platform for Expedited Synthesis-Purification-Testing of Small Molecule Libraries | Q36354649 | ||
| Low Data Drug Discovery with One-Shot Learning | Q36362630 | ||
| P433 | issue | 2 | |
| P921 | main subject | automation | Q184199 |
| drug discovery | Q1418791 | ||
| P304 | page(s) | 97-113 | |
| P577 | publication date | 2017-12-15 | |
| P13046 | publication type of scholarly work | review article | Q7318358 |
| P1433 | published in | Nature Reviews Drug Discovery | Q45998 |
| P1476 | title | Automating drug discovery | |
| P478 | volume | 17 |
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