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RESEARCH PAPER · Nature Chemistry · 2023

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

David F. Nippa, Kenneth Atz, Remo Hohler, Alex T. Müller, Andreas Marx, Christian Bartelmus, Georg Wuitschik, Irene Marzuoli, Vera Jost, Jens Wolfard, Martin Binder, Antonia F. Stepan, David Benjamin Konrad, Uwe Grether, Rainer E. Martin, Gisbert Schneider

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Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learningOriginalPutting Chemical Knowledge to Work in Machine Learning for Reactivity12Structure-based drug design with geometric deep learning178Graph transformer neural network for chemical reactivity prediction3Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation71Deep interactome learning for de novo drug design3High-throughput synthesis provides data for predicting molecular properties and reaction success28Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening16Dataset Design for Building Models of Chemical Reactivity94Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR397AI for Health and Medicine0Combining de novo molecular design with semiempirical protein–ligand binding free energy calculation7Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning26Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry11Highlights from the 57th Bürgenstock Conference on Stereochemistry 20240Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network8Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions18Thermal/photochemical micro-flow probe system for direct C–H bond functionalization of biologically active molecules4Predictive Minisci late stage functionalization with transfer learning29Predicting Late-Stage C–H Borylations of Drug Molecules0Prospective de novo drug design with deep interactome learning96Leveraging Language Model Multitasking To Predict C–H Borylation Selectivity7Drug discovery and development in the era of artificial intelligence: From machine learning to large language models36Predicting Three-Component Reaction Outcomes from 40k Miniaturized Reactant Combinations2HTE OS: A High-Throughput Experimentation Workflow Built from the Ground Up15Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15Do we really need ligands in Ir-catalyzed C–H borylation?3DNA‐Compatible Nitriles Hydrolysis for Late‐Stage Functionalization of DNA‐Encoded Libraries6Meeting report of the 5th European Biotransformation Workshop2Artificial intelligence: empowering new insights into gamete and embryo development0We are MedChem: The Frontiers in Medicinal Chemistry 20240Reinforcement Learning for Improving Chemical Reaction Performance15Drugs from drugs: New chemical insights into a mature concept11Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry34The path to adoption of open source AI for drug discovery in Africa0Calibration-free quantification and automated data analysis for high-throughput reaction screening5The Theranostic Genome12Accelerated click reactions using boronic acids for heterocyclic synthesis in microdroplets15Accelerating compound synthesis in drug discovery: the role of digitalisation and automation4Computational tools for the prediction of site- and regioselectivity of organic reactions33Design and synthesis of photoswitchable desloratadine ligands for histamine H 1 receptor photopharmacology5ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds4Molecular Machine Learning Approach to Enantioselective C–H Bond Activation Reactions: From Generative AI to Experimental Validation5Predicting reaction conditions: a data-driven perspective10Recommending reaction conditions with label ranking6Standardized Approach for Diversification of Complex Small Molecules via Aryl Thianthrenium Salts48High-Throughput Enabled Iridium-Catalyzed C–H Borylation Platform for Late-Stage Functionalization9Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates18Application of Density Functional Theory to Molecular Engineering of Pharmaceutical Formulations112Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery20Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation19A meta-learning approach for selectivity prediction in asymmetric catalysis11Selective C–H Borylation of Polyaromatic Compounds Enabled by Metal-Arene π-Complexation9The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations6Bridging 3D Molecular Structures and Artificial Intelligence by a Conformation Description Language0Production and evaluation of high-throughput reaction data from an automated chemical synthesis platform5Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations14TP-Transformer: An Interpretable Model for Predicting the Transformation Pathways of Organic Pollutants in Chemical Oxidation Processes24Predicting Reactivity with Machine Learning1Synthesis of Semi-Saturated Heterocycles via Photocatalytic Intramolecular [2 + 2] Cycloaddition/Dearomatization5Base-Mediated Alkylation of Pyrimidines via Sulfur Deletion1Sterically Controlled C–H Borylation and Silylation of (Hetero)arenes Using Pyrazolonaphthyridine Ligands1Geometric Deep Learning for Protein-Ligand Affinity Prediction With Hybrid Message Passing Strategies8Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery4Tech-Enhanced Synthesis: Exploring the Synergy between Organic Chemistry and Technology45Using High‐Throughput Iridium‐Catalyzed C─H Borylations for Rapid Investigation of Heterocyclic Fragments in Discovery Chemistry0Empowering Reactivity Predictions through Noise-Based Data Augmentation15Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach6Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach1Progress of AI-Driven Drug–Target Interaction Prediction and Lead Optimization23Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization9Solvent Sustainability in Drug Discovery: Where Are We Now, and How Can We Improve?25An overview of reaction outcome prediction with physics-based and data-driven methods2Innovation-driven drug discovery: Frontier exploration and breakthrough0Photochemical skeletal editing: conceptual advances in C–C and C–heteroatom bond activation9Physics-informed conformal prediction of aromatic C–H site selectivity: guaranteed coverage prediction sets for electrophilic aromatic substitution0Reaction Prediction Can Change How Natural Product Scaffolds Are Optimized by Connecting Biosynthetic Logic, Semisynthesis, and Medicinal Chemistry Search0Developments and challenges in hit progression within fragment-based drug discovery11Artificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation13Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments2A review on the advances in non-application specific laboratory automation2High-throughput reaction screening using acoustic ejection mass spectrometry0Regioselective Iridium-Catalyzed C(7)–H Borylation of Free N –H 6-Fluoroquinolones0Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language2Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations0Functional Conjugable Diselenides through Sequential Dehydrogenative Dimerization Cascade03D, Structure-Based, Deep Learning Approach to Predict the Regioselectivity of Olefin Hydroformylations0AI-Predicted Full-Spectrum UV Correction Factors: An Analyte-Standard-Free Framework for Reaction Yield Quantification0Secondary amines derived from salicylaldehyde 8-aminoquinoline: From structural flexibility to antimicrobial activity, biomolecular binding and developmental toxicity in vivo0Advancing chemical reaction prediction in data-scarce drug discovery with active and geometric deep learning1

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  2. Structure-based drug design with geometric deep learning

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  3. Graph transformer neural network for chemical reactivity prediction

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  4. Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

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  5. Deep interactome learning for de novo drug design

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  6. High-throughput synthesis provides data for predicting molecular properties and reaction success

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  7. Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

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  8. Dataset Design for Building Models of Chemical Reactivity

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  9. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

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  10. AI for Health and Medicine

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  12. Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning

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  13. Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry

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  16. Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

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  17. Thermal/photochemical micro-flow probe system for direct C–H bond functionalization of biologically active molecules

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  18. Predictive Minisci late stage functionalization with transfer learning

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  19. Predicting Late-Stage C–H Borylations of Drug Molecules

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  20. Prospective de novo drug design with deep interactome learning

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  21. Leveraging Language Model Multitasking To Predict C–H Borylation Selectivity

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  23. Predicting Three-Component Reaction Outcomes from 40k Miniaturized Reactant Combinations

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  24. HTE OS: A High-Throughput Experimentation Workflow Built from the Ground Up

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  26. Do we really need ligands in Ir-catalyzed C–H borylation?

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