Deep learning for predicting pharmacological properties

network diagram

Abstract

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. We demonstrate how deep neural networks trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles.

We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to twelve therapeutic use categories derived from MeSH. To train the deep neural network, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours.

In both pathway and gene level classification, the deep neural network achieved high classification accuracy and convincingly outperformed the support vector machine model on every multiclass classification problem, however, models based on pathway level data performed significantly better.

For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

FULL TEXT: Molecular Pharmaceutics

Human gut microbiome aging clock

cup and alarm clock

Abstract

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host’s age based on the gut community composition.

Here we developed a method of predicting hosts’ age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers.

The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

FULL TEXT (PDF): iScience

Aging clocks: AI-based biomarkers of aging

an alarm clock

Excerpt

First published in 2016, predictors of chronological and biological age developed using deep learning are rapidly gaining popularity in the aging research community. These deep aging clocks [that have been developed using artificial intelligence (AI)] can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation.

Recent advances in machine learning, coupled with increases in computational power and availability of the large publicly available datasets, have led to a renaissance in AI. These advances have generated substantial investment and hype, and many data scientists and companies are exploiting the surge in AI hype for promotional purposes. This has sown confusion in the market and triggered criticism from scientists working in the pharmaceutical industry, where approval in clinical trials is the ultimate measure of success.

Most of the credible advances in the field have been in deep learning and reinforcement learning. Since 2013, deep learning systems have surpassed human performance in multiple applications, including strategy games as well as image and text recognition. In healthcare, deep learning systems outperformed human dermatologists, ophthalmologists, and radiologists in various tasks. Deep learning also demonstrated significant improvement over conventional machine learning methods in biomedical data analysis.

Biomarkers of aging

During this same period of deep learning progress, aging research has also experienced a renaissance, and new breakthroughs are rapidly emerging. Multiple data types can be used to predict age and associate the prediction with mortality, disease, general well-being, or other biological processes including methylation, gene expression, microbiome, and imaging data.

Since the publication of the first multitissue methylation aging clock by Steven Horvath in 2013, multiple methylation aging clocks and applications of these clocks in humans and mice were developed. Even though these clocks were developed using traditional machine learning approaches – notably linear regression with regularization and the use of a limited number of samples – the results suggest that gradual changes during aging can be tracked using various data types with reasonable accuracy.

FULL TEXT: Trends in Pharmacological Sciences

Neural networks and deep learning

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We’ll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they’ve become one of the most dominant machine learning technologies used today.

Neural Networks and Deep Learning

A book online

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started.