All Stories

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