Artificial Intelligence in Toxicology and Pharmacology

References (85)

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2. 2 ChEMBL: towards direct deposition of bioassay data [DOI]
3. 3 Robust Physical-World Attacks on Deep Learning Visual Classification [DOI]
4. 4 Inverse molecular design using machine learning: Generative models for matter engineering [DOI]
5. 5 Planning chemical syntheses with deep neural networks and symbolic AI [DOI]
6. 6 The rise of deep learning in drug discovery [DOI]
7. 7 Molecular graph convolutions: moving beyond fingerprints [DOI]
8. 8 Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks [DOI]
9. 9 Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 [DOI]
10. 10 Molecular de-novo design through deep reinforcement learning [DOI]
11. 11 Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships [DOI]
12. 12 A Mathematical Contribution to Structure-Activity Studies [DOI]
13. 13 Automating drug discovery [DOI]
14. 14 Hidden technical debt in Machine learning systems
15. 15 Low Data Drug Discovery with One-Shot Learning [DOI]
16. 16 Machine Learning in Computer-Aided Synthesis Planning [DOI]
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18. 18 A graph-convolutional neural network model for the prediction of chemical reactivity [DOI]
19. 19 Artificial intelligence, machine learning and health systems [DOI]
20. 20 DNA-encoded chemistry: enabling the deeper sampling of chemical space [DOI]
21. 21 Large-scale comparison of machine learning methods for drug target prediction on ChEMBL [DOI]
22. 22 How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR) [DOI]
23. 23 Artificial intelligence in medicine: current trends and future possibilities [DOI]
24. 24 Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR [DOI]
25. 25 ImageNet: Constructing a large-scale image database [DOI]
26. 26 Is Multitask Deep Learning Practical for Pharma? [DOI]
27. 27 Active-learning strategies in computer-assisted drug discovery [DOI]
28. 28 Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction [DOI]
29. 29 The next level in chemical space navigation: going far beyond enumerable compound libraries [DOI]
30. 30 ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting [DOI]
31. 31 Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity [DOI]
32. 32 New drugs cost US$2.6 billion to develop [DOI]
33. 33 Computational prediction of chemical reactions: current status and outlook [DOI]
34. 34 Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? [DOI]
35. 35 Data integration: challenges for drug discovery [DOI]
36. 36 Cybernetics and forecasting techniques
37. 37 Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors [DOI]
38. 38 Imputation of Assay Bioactivity Data Using Deep Learning [DOI]
39. 39 The advancement of multidimensional QSAR for novel drug discovery - where are we headed? [DOI]
40. 40 Clinical Toxicology: Principles and Mechanisms
41. 41 Environmental Chemical Assessment in Clinical Practice: Unveiling the Elephant in the Room [DOI]
42. 42 Estimation of the applicability domain of kernel-based machine learning models for virtual screening [DOI]
43. 43 Virtual Screening Using Binary Kernel Discrimination: Analysis of Pesticide Data [DOI]
44. 44 Quantitative structure/property relationship analysis of Caco‐2 permeability using a genetic algorithm‐based partial least squares method [DOI]
45. 45 ChemInform Abstract: Validation of Reaction Vectors for de novo Design [DOI]
46. 46 Junction Tree Variational Autoencoder for Molecular Graph Generation [DOI]
47. 47 Extraction of chemical structures and reactions from the literature [DOI]
48. 48 Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) [DOI]
49. 49 The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
50. 50 Adversarial Controls for Scientific Machine Learning [DOI]
51. 51 Applications of machine learning in drug discovery and development [DOI]
52. 52 Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. [DOI]
53. 53 Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery [DOI]
54. 54 Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model [DOI]
55. 55 Rethinking drug design in the artificial intelligence era [DOI]
56. 56 Deep learning for molecular design—a review of the state of the art [DOI]
57. 57 Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions [DOI]
58. 58 A de novo molecular generation method using latent vector based generative adversarial network [DOI]
59. 59 Artificial Intelligence for Drug Toxicity and Safety [DOI]
60. 60 Why deep-learning AIs are so easy to fool [DOI]
61. 61 Synthetic organic chemistry driven by artificial intelligence [DOI]
62. 62 A Deep Learning Approach to Antibiotic Discovery [DOI]
63. 63 Assessing the impact of generative AI on medicinal chemistry [DOI]
64. 64 Predicting drug-target interaction network using deep learning model [DOI]
65. 65 Biosystems Design by Machine Learning [DOI]
66. 66 Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks [DOI]
67. 67 Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery [DOI]
68. 68 Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction [DOI]
69. 69 BRADSHAW: a system for automated molecular design [DOI]
70. 70 Perspective on model‐informed drug development [DOI]
71. 71 An Introduction to Interdisciplinary Toxicology [DOI]
72. 72 Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning [DOI]
73. 73 Role of Disease Progression Models in Drug Development [DOI]
74. 74 A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform [DOI]
75. 75 Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform [DOI]
76. 76 The Role and Promise of Artificial Intelligence in Medical Toxicology [DOI]
77. 77 Enhancing reaction-based de novo design using a multi-label reaction class recommender [DOI]
78. 78 Model‐Informed Drug Development for Everolimus Dosing Selection in Pediatric Infant Patients [DOI]
79. 79 In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions [DOI]
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81. 81 Clinical toxicology and clinical analytical toxicology [DOI]
82. 82 A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity [DOI]
83. 83 Modeling Physico-Chemical ADMET Endpoints With Multitask Graph Convolutional Networks [DOI]
84. 84 AI developers tout revolution, drugmakers talk evolution [DOI]
85. 85 De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence [DOI]

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