  Artificial Neural Networks (ANN) have been popularized in many science and
technological areas due to their capacity to solve many complex pattern
matching problems. That is the case of Virtual Screening, a research area that
studies how to identify those molecular compounds with the highest probability
to present biological activity for a therapeutic target. Due to the vast number
of small organic compounds and the thousands of targets for which such
large-scale screening can potentially be carried out, there has been an
increasing interest in the research community to increase both, processing
speed and energy efficiency in the screening of molecular databases. In this
work, we present a classification model describing each molecule with a single
energy-based vector and propose a machine-learning system based on the use of
ANNs. Different ANNs are studied with respect to their suitability to identify
biochemical similarities. Also, a high-performance and energy-efficient
hardware acceleration platform based on the use of stochastic computing is
proposed for the ANN implementation. This platform is of utility when screening
vast libraries of compounds. As a result, the proposed model showed appreciable
improvements with respect previously published works in terms of the main
relevant characteristics (accuracy, speed and energy-efficiency).
