Artificial Neural Network–Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds

• Published in: SLAS discovery 2009-12, Vol.14 (10), p.1236-1244
• Main Authors: Chakrabarti, Swapan, Svojanovsky, Stan R., Slavik, Romana, Georg, Gunda I., Wilson, George S., Smith, Peter G.
• Format: Article
• Language: English
• Subjects: generalization property; neural networks; pattern classification
• ISSN: 2472-5552; 2472-5560
• DOI: 10.1177/1087057109351312

Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, RA/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in RA/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.