“In-House Likeness”: Comparison of Large Compound Collections Using Artificial Neural Networks

Binary classification models able to discriminate between data sets of compounds are useful tools in a
range of applications from compound acquisition to library design. In this paper we investigate the ability
of artificial neural networks to discriminate between compound collections from various sources aiming at
developing an “in-house likeness” scoring scheme (i.e. in-house vs external compounds) for compound
acquisition. Our analysis shows atom-type based Ghose-Crippen fingerprints in combination with artificial
neural networks to be an efficient way to construct such filters. A simple measure of the chemical overlap
between different compound collections can be derived using the output scores from the neural net models.