Over the last decade neural networks have become an efficient method for data analysis in the field of drug discovery. The early problems encountered with neural networks such as overfitting and overtraining have been addressed resulting in a technique that surpasses traditional statistical methods. Neural networks have thus largely lived up to their promise, which was to overcome QSAR statistical problems. The next revolution in QSAR will no doubt involve research into producing better descriptors used in these studies to improve our ability to relate chemical structure to biological activity. This review focuses on the applications of neural network methods and their development over the last five years.
- Back-propagation neural network
- Genetic algorithm
- Kohonen neural network
- Multiple linear regression
- Quantitative structure-activity relationship