| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
AI for drug design: From explicit rules to deep learning
|
Mervin, Lewis
|
|
|
2 |
C |
p.
|
artikel
|
| 2 |
AI in Life Science Research – The Road Ahead
|
Bajorath, Jürgen
|
|
|
2 |
C |
p.
|
artikel
|
| 3 |
An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis
|
Menestrina, Luca
|
|
|
2 |
C |
p.
|
artikel
|
| 4 |
Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods
|
Yang, Zhenwu
|
|
|
2 |
C |
p.
|
artikel
|
| 5 |
Corrigendum to “Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases”[Artificial Intelligence in Life Sciences] 1(2021), 100020
|
Linden, Thomas
|
|
|
2 |
C |
p.
|
artikel
|
| 6 |
Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?
|
Erjavac, Ivan
|
|
|
2 |
C |
p.
|
artikel
|
| 7 |
Deepitope: Prediction of HLA-independent T-cell epitopes mediated by MHC class II using a convolutional neural network
|
Trevizani, Raphael
|
|
|
2 |
C |
p.
|
artikel
|
| 8 |
Deep learning of protein–ligand interactions—Remembering the actors
|
Bajorath, Jürgen
|
|
|
2 |
C |
p.
|
artikel
|
| 9 |
Editorial Board
|
|
|
|
2 |
C |
p.
|
artikel
|
| 10 |
HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning
|
Tripathi, Satvik
|
|
|
2 |
C |
p.
|
artikel
|
| 11 |
Interpretation of multi-task clearance models from molecular images supported by experimental design
|
Martínez Mora, Andrés
|
|
|
2 |
C |
p.
|
artikel
|
| 12 |
LIDeB Tools: A Latin American resource of freely available, open-source cheminformatics apps
|
Gori, Denis N. Prada
|
|
|
2 |
C |
p.
|
artikel
|
| 13 |
Modeling bioconcentration factors in fish with explainable deep learning
|
Zhao, Linlin
|
|
|
2 |
C |
p.
|
artikel
|
| 14 |
Open protocols for docking and MD-based scoring of peptide substrates
|
Ochoa, Rodrigo
|
|
|
2 |
C |
p.
|
artikel
|
| 15 |
Optimizing active learning for free energy calculations
|
Thompson, James
|
|
|
2 |
C |
p.
|
artikel
|
| 16 |
Recent advances and application of generative adversarial networks in drug discovery, development, and targeting
|
Tripathi, Satvik
|
|
|
2 |
C |
p.
|
artikel
|
| 17 |
Revisiting active learning in drug discovery through open science
|
Bajorath, Jürgen
|
|
|
2 |
C |
p.
|
artikel
|
| 18 |
Symbolic regression for the interpretation of quantitative structure-property relationships
|
Takaki, Katsushi
|
|
|
2 |
C |
p.
|
artikel
|
| 19 |
SyntaLinker-Hybrid: A deep learning approach for target specific drug design
|
Feng, Yu
|
|
|
2 |
C |
p.
|
artikel
|
| 20 |
The commoditization of AI for molecule design
|
Urbina, Fabio
|
|
|
2 |
C |
p.
|
artikel
|
| 21 |
Understanding the performance of knowledge graph embeddings in drug discovery
|
Bonner, Stephen
|
|
|
2 |
C |
p.
|
artikel
|
| 22 |
Understanding uncertainty in deep learning builds confidence
|
Bajorath, Jürgen
|
|
|
2 |
C |
p.
|
artikel
|
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