All Stories

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