All Stories

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