All Stories

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