252 results on '"Reker, Daniel"'
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2. The landscape of small-molecule prodrugs
3. Machine learning trims the peptide drug design process to a sweet spot
4. Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning
5. Characterizing emerging companies in computational drug development
6. Artificial intelligence for natural product drug discovery
7. Yoked learning in molecular data science
8. Angiopoietin-2 is associated with capillary leak and predicts complications after cardiac surgery
9. DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning
10. Combating small molecule aggregation with machine learning
11. Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery
12. Abstract 11618: Angiopoietin-2 is Associated With Capillary Leak and Complications After Cardiac Surgery
13. Computationally guided high-throughput design of self-assembling drug nanoparticles
14. Artificial intelligence for natural product drug discovery
15. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information
16. Leveraging bounded datapoints to classify molecular potency improvements.
17. Data-driven learning of structure augments quantitative prediction of biological responses.
18. Cheminformatic Analysis of Natural Product Fragments
19. Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection
20. Practical considerations for active machine learning in drug discovery
21. Artificial intelligence in chemistry and drug design
22. Robotically handled whole-tissue culture system for the screening of oral drug formulations
23. Yoked Learning in Molecular Data Science
24. Computational advances in combating colloidal aggregation in drug discovery
25. Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications
26. DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning
27. Interpretable Molecular Property Predictions Using Marginalized Graph Kernels
28. Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning
29. Leveraging bounded datapoints to classify molecular potency improvementsElectronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4md00325j
30. Selection of Informative Examples in Chemogenomic Datasets
31. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees
32. Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.
33. Active-learning strategies in computer-assisted drug discovery
34. Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning
35. A machine learning liver-on-a-chip system for safer drug formulation
36. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus
37. Common non-epigenetic drugs as epigenetic modulators
38. Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors
39. Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections
40. Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors
41. Combating small-molecule aggregation with machine learning
42. Vaccination with anti-idiotype antibody ganglidiomab mediates a GD2-specific anti-neuroblastoma immune response
43. Additional file 4 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection
44. Additional file 5 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection
45. Additional file 1 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection
46. Additional file 3 of Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection
47. Artificial intelligence in chemistry and drug design
48. Machine Learning Uncovers Food- and Excipient-Drug Interactions
49. Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications
50. Practical considerations for active machine learning in drug discovery
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