All Stories

  1. Informatics in Control, Automation and Robotics
  2. Chemical Language Models for chemical design and property prediction
  3. Introduction to Machine Learning for Materials Property Modeling
  4. QSAR: Using the Past to Study the Present
  5. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project
  6. Big data and deep learning: extracting and revising chemical knowledge from data
  7. Informatics in Control, Automation and Robotics
  8. Active upper limb prostheses: a review on current state and upcoming breakthroughs
  9. Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
  10. Correction to Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
  11. Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors
  12. Predictive models of toxicity using deep learning: the case of mutagenicity
  13. Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
  14. Can chemical similarity be computed by deep learning?
  15. Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
  16. Discovering substructures able to predicting toxicity against fish
  17. A neuromorphic control architecture for a biped robot
  18. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy
  19. Could deep learning in neural networks improve the QSAR models?
  20. Constructing knowledge bases for robots
  21. A robot cognitive model learns which action to select
  22. Programs that predict mutagenicity of chemical compouds and their integration
  23. Classify shoulder movements from sEMG signals
  24. Understanting the computational methods for predicting the biological properties of chemicals
  25. Advances in QSAR Modeling
  26. Comparing expert read-across predictions
  27. From learning to new goal generation in a bioinspired robotic setup
  28. New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds
  29. ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals
  30. Molecular substructures linked to ready biodegradability of chemicals
  31. Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction
  32. CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats
  33. Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships
  34. ChemInform Abstract: The Importance of Scaling in Data Mining for Toxicity Prediction.
  35. GUEST EDITORIAL: MARCO SOMALVICO MEMORIAL ISSUE
  36. GUEST EDITORIAL: AN ARTIFICIAL INTELLIGENCE MISCELLANEA, REMEMBERING MARCO SOMALVICO
  37. Guest editorial: Marco Somalvico memorial issue
  38. GUEST EDITORIAL: PAPERS IN SENSING AND IN REASONING (MARCO SOMALVICO MEMORIAL ISSUE)
  39. E-MODELLING: FOUNDATIONS AND CASES FOR APPLYING AI TO LIFE SCIENCES
  40. Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs
  41. LARP, Biped Robotics Conceived as Human Modelling
  42. MULTICLASS CLASSIFIER FROM A COMBINATION OF LOCAL EXPERTS: TOWARD DISTRIBUTED COMPUTATION FOR REAL-PROBLEM CLASSIFIERS
  43. Database mining with adaptive fuzzy partition: Application to the prediction of pesticide toxicity on rats
  44. The Importance of Scaling in Data Mining for Toxicity Prediction
  45. Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
  46. Robotic programs for manipulation can use a frame-based model of the world
  47. Interactive development of object handling programs
  48. Advanced steps in biped robotics: innovative design and intuitive control through spring-damper actuator
  49. Clustering and classification techniques to assess aquatic toxicity