Name
Abstract | ||
|---|---|---|
In de novo drug design, chemical compounds are quantitized as real-valued vectors called chemical descriptors, and an optimization algorithm runs on known drug-like chemical compounds in a database and outputs an optimal chemical descriptor. Since structural information is needed for chemical synthesis, we must infer chemical graphs from the obtained descriptor. This is formalized as a graph inference problem from a real-value vector. By generalizing subword history, which was originally introduced in formal language theory to extract numerical information of words and languages based on counting, we propose a comprehensive framework to investigate the computational complexity of chemical graph inference. We also propose a (pseudo-)polynomial-time algorithm for inferring graphs in a class of practical importance from spectrums. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11047-012-9349-2 | Natural Computing |
Keywords | Field | DocType |
Computational complexity,Counting,de novo drug design,Graph inference,Tree-decomposition,Spectrum,Walk history | Graph,Formal language,Computer science,Generalization,Inference,Tree decomposition,Algorithm,Theoretical computer science,Optimization algorithm,Artificial intelligence,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
12 | 4 | 1567-7818 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Szilárd Zsolt Fazekas | 1 | 14 | 7.40 |
| Hiro Ito | 2 | 290 | 39.95 |
| Yasushi Okuno | 3 | 604 | 125.54 |
| Shinnosuke Seki | 4 | 189 | 29.78 |
| Kei Taneishi | 5 | 15 | 3.45 |