Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms

Recently neural networks have been applied with some success to the study of quantitative structure activity relationships. One limitation of their use is that, while they are able to predict the biological activity of a new molecule from its physicochemical properties, it is difficult to get them to solve the more interesting problem of predicting the required molecular properties of a more active molecule. This paper proposes one method for solving this problem by using genetic algorithms and explores their potential as a method for solving this problem. Suggestions for more potent dihydrofolate reductase inhibitors are made.