𝔖 Bobbio Scriptorium
✦   LIBER   ✦

ANVAS: Artificial Neural Variables Adaptation System for descriptor selection

✍ Scribed by Paolo Mazzatorta; Marjan Vračko; Emilio Benfenati

Book ID
111557222
Publisher
Springer Netherlands
Year
2003
Tongue
English
Weight
382 KB
Volume
17
Category
Article
ISSN
0920-654X

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Selective descriptor pruning for QSAR/QS
Selective descriptor pruning for QSAR/QSPR studies using artificial neural networks
✍ Joseph V. Turner; David J. Cutler; Ian Spence; Desmond J. Maddalena 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 105 KB

## Abstract Selection of optimal descriptors in quantitative structure–activity–property relationship (QSAR/QSPR) studies has been a perennial problem. Artificial Neural Networks (ANNs) have been used widely in QSAR/QSPR studies but less widely in descriptor selection. The current study used ANNs t

A self-adaptive genetic algorithm-artifi
A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study
✍ Jingheng Wu; Juan Mei; Sixiang Wen; Siyan Liao; Jincan Chen; Yong Shen 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 452 KB

## Abstract Based on the quantitative structure‐activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable‐selection approach with molecule descriptors and helped to improve the back‐propagation training algorithm as well. T