Wiktor Beker
Polish chemist and researcher
Facts
8 properties
🏷️ Also known as
en Wiktor Beker
en Wiktor Lech Beker
🔖 Identifiers · 4
- ORCID iD
- 0000-0002-2913-6724
- Dimensions author ID
- 01043536602.31
- Ludzie Nauki ID
- bVFhWkiL2O3
- Nauka Polska ID (archival)
- 286561
🔗 Connections
Author of 7
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling,
Computational prediction of complex cationic rearrangement outcomes,
Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling,
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks,
Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors,
Rapid and Accurate Prediction of p<i>K</i><sub>a</sub> Values of C–H Acids Using Graph Convolutional Neural Networks,
Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions
← Authored by 7
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling,
Computational prediction of complex cationic rearrangement outcomes,
Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling,
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks,
Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors,
Rapid and Accurate Prediction of p<i>K</i><sub>a</sub> Values of C–H Acids Using Graph Convolutional Neural Networks,
Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions
Press Enter · cited answer in seconds