All Stories

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