All Stories

  1. Democratizing Reaction Kinetics through Machine Vision and Learning
  2. Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions
  3. Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer
  4. AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
  5. Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials
  6. Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
  7. Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
  8. All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models
  9. Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy
  10. Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning
  11. Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning
  12. AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale
  13. Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry
  14. ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules
  15. Machine Learning anomaly detection of automated HPLC experiments in the Cloud Laboratory
  16. Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
  17. All that glitters is not gold: Importance of rigorous evaluation of proteochemometric models
  18. AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs
  19. High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning
  20. GEOM-drugs revisited: toward more chemically accurate benchmarks for 3D molecule generation
  21. Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory
  22. AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
  23. Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
  24. ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials
  25. Discovery of Crystallizable Organic Semiconductors with Machine Learning
  26. Discovery of Crystallizable Organic Semiconductors with Machine Learning
  27. AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
  28. Discovery of Crystallizable Organic Semiconductors with Machine Learning
  29. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
  30. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
  31. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
  32. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
  33. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
  34. AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
  35. Synergy of semiempirical models and machine learning in computational chemistry
  36. The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
  37. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
  38. Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)
  39. Generative Models as an Emerging Paradigm in the Chemical Sciences
  40. Machine Learning Interatomic Potentials and Long-Range Physics
  41. Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling
  42. Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
  43. The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions
  44. Themed collection on Insightful Machine Learning for Physical Chemistry
  45. Δ2 machine learning for reaction property prediction
  46. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds
  47. Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials
  48. Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials
  49. Extending machine learning beyond interatomic potentials for predicting molecular properties
  50. Active learning guided drug design lead optimization based on relative binding free energy modeling
  51. Simulations of Pathogenic E1α Variants: Allostery and Impact on Pyruvate Dehydrogenase Complex-E1 Structure and Function
  52. Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials
  53. Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods
  54. The transformational role of GPU computing and deep learning in drug discovery
  55. Prediction of Protein pKa with Representation Learning
  56. Prediction of Protein pKa with Representation Learning
  57. Prediction of protein pKa with representation learning
  58. Artificial intelligence-enhanced quantum chemical method with broad applicability
  59. Prediction of Protein pKa with Representation Learning
  60. Prediction of Protein pKa with Representation Learning
  61. Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
  62. Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
  63. Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World
  64. Teaching a neural network to attach and detach electrons from molecules
  65. Learning molecular potentials with neural networks
  66. Machine learned Hückel theory: Interfacing physics and deep neural networks
  67. Crowdsourced mapping of unexplored target space of kinase inhibitors
  68. Best practices in machine learning for chemistry
  69. Teaching a Neural Network to Attach and Detach Electrons from Molecules
  70. Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence
  71. A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery
  72. A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery
  73. OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
  74. A critical overview of computational approaches employed for COVID-19 drug discovery
  75. High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications
  76. Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials
  77. Teaching a Neural Network to Attach and Detach Electrons from Molecules
  78. OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
  79. DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
  80. DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
  81. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
  82. DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
  83. Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
  84. Review for: Assessing Conformer Energies using Electronic Structure and Machine Learning Methods
  85. TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials
  86. The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
  87. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
  88. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
  89. Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
  90. Crowdsourced mapping of unexplored target space of kinase inhibitors
  91. Correction: QSAR without borders
  92. QSAR without borders
  93. DRACON: disconnected graph neural network for atom mapping in chemical reactions
  94. Predicting Thermal Properties of Crystals Using Machine Learning
  95. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
  96. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
  97. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
  98. Text mining facilitates materials discovery
  99. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
  100. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
  101. Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects
  102. Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides
  103. Adsorption of nitrogen-containing compounds on hydroxylated α-quartz surfaces
  104. Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches
  105. Transforming Computational Drug Discovery with Machine Learning and AI
  106. Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
  107. Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
  108. Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
  109. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
  110. Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches
  111. Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
  112. Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches
  113. Discovering a Transferable Charge Assignment Model Using Machine Learning
  114. Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning
  115. Deep reinforcement learning for de novo drug design
  116. Machine learning for molecular and materials science
  117. Less is more: Sampling chemical space with active learning
  118. Discovering a Transferable Charge Assignment Model Using Machine Learning
  119. Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning
  120. Diffusion of energetic compounds through biological membrane: application of classical MD and COSMOmic approximations
  121. Materials discovery by chemical analogy: role of oxidation states in structure prediction
  122. Outsmarting Quantum Chemistry Through Transfer Learning
  123. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
  124. Universal fragment descriptors for predicting properties of inorganic crystals
  125. Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode
  126. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
  127. Atlas Regeneration Company, Inc.
  128. QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays
  129. Are the reduction and oxidation properties of nitrocompounds dissolved in water different from those produced when adsorbed on a silica surface? A DFT M05-2X computational study
  130. Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
  131. In silico structure-function analysis of E. cloacae nitroreductase
  132. Mechanical properties of silicon nanowires
  133. Validation of a novel secretion modification region (SMR) of HIV-1 Nef using cohort sequence analysis and molecular modeling
  134. Evaluation of natural and nitramine binding energies to 3-D models of the S1S2 domains in the N-methyl-D-aspartate receptor
  135. Car–Parrinello Molecular Dynamics Simulations of Tensile Tests on Si⟨001⟩ Nanowires
  136. Effect of Solvation on the Vertical Ionization Energy of Thymine: From Microhydration to Bulk
  137. Toward robust computational electrochemical predicting the environmental fate of organic pollutants
  138. Novel view on the mechanism of water-assisted proton transfer in the DNA bases: bulk water hydration
  139. Reaction of bicyclo[2.2.1]hept-5-ene-endo-2-ylmethylamine and nitrophenyl glycidyl ethers
  140. One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosives
  141. Hydration of nucleic acid bases: a Car–Parrinello molecular dynamics approach
  142. New insight on structural properties of hydrated nucleic acid bases from ab initio molecular dynamics
  143. Ab Initio Molecular Dynamics Study on the Initial Chemical Events in Nitramines: Thermal Decomposition of CL-20
  144. Efficient and accurate ab initio prediction of thermodynamic parameters for intermolecular complexes
  145. Carboxamides and amines having two and three adamantane fragments
  146. Electronic Structure and Bonding of {Fe(PhNO2)}6 Complexes:  A Density Functional Theory Study
  147. Are Isolated Nucleic Acid Bases Really Planar? A Car−Parrinello Molecular Dynamics Study
  148. Theoretical calculations: Can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately?
  149. Structure-toxicity relationships of nitroaromatic compounds
  150. Acylation of Aminopyridines and Related Compounds with Endic Anhydride
  151. Synthesis and Reactivity of Amines Containing Several Cage-like Fragments
  152. Amides containing two norbornene fragments. Synthesis and chemical transformations
  153. Reaction of Endic Anhydride with Hydrazines and Acylhydrazines
  154. Modeling the Gas-Phase Reduction of Nitrobenzene to Nitrosobenzene by Iron Monoxide:  A Density Functional Theory Study
  155. Amino Alcohols with Bicyclic Carbon Skeleton. Alternative Functionalization of Nucleophilic Reaction Centers