Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening

TL;DR

This paper introduces a stochastic computing-based hardware acceleration platform for neural networks to improve the speed and energy efficiency of ligand-based virtual screening, enabling faster analysis of large molecular libraries.

Contribution

It proposes a novel hardware acceleration platform using stochastic computing for ANNs, enhancing virtual screening efficiency and accuracy over previous methods.

Findings

Improved processing speed and energy efficiency in virtual screening.

Effective neural network models for biochemical similarity detection.

Hardware platform demonstrates practical utility for large-scale compound screening.

Abstract

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).