All Stories

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