State-of-the-art in artificial neural network applications: A survey

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.

ADMET in silico modelling: towards prediction paradise?

In the absence of effective vaccines to control herpesvirus infections, nucleosidic antiviral drugs have been the mainstay of clinical treatment since their development in the late 1970s. However, given the drawbacks of these drugs, including the increasing emergence of drug-resistant clinical isolates, new strategies for treating herpesvirus infections are warranted. A range of promising new drugs with novel molecular targets has been developed, but will they cure latent infections?

Antiherpesvirus drugs: a promising spectrum of new drugs and drug targets

Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of “deep learning”. Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

R. Beresford

Papers

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

Theoretically-derived molecular descriptors important in human intestinal absorption.

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

Determination of enantiomeric composition of ibuprofen in solid state mixtures of the two by DRIFT spectroscopy