Since 2010 deep learning systems demonstrated unprecedented results
in image, voice and text recognition, in many cases surpassing human
accuracy and enabling autonomous driving, automated creation of pleasant
art and even composition of pleasant music.
Scientists at the Pharmaceutical Artificial Intelligence (pharma.AI)
group of Insilico Medicine, Inc, today announced the publication of a
seminal paper demonstrating the application of generative adversarial
autoencoders (AAEs) to generating new molecular fingerprints on demand.
‘The application of generative adversarial autoencoders (AAEs) to generate new molecular fingerprints on demand has been demonstrated by researchers.’
The study was published in Oncotarget
. The study represents the proof of concept
for applying Generative Adversarial Networks (GANs) to drug discovery.
The authors significantly extended this model to generate new leads
according to multiple requested characteristics and plan to launch a
comprehensive GAN-based drug discovery engine producing promising
therapeutic treatments to significantly accelerate pharmaceutical
R&D and improve the success rates in clinical trials.
GAN is a fresh direction in deep learning invented by Ian Goodfellow
in 2014. In recent years GANs produced extraordinary results in
generating meaningful images according to the desired descriptions.
Similar principles can be applied to drug discovery and biomarker
This paper represents a proof of concept of an
artificially-intelligent drug discovery engine, where AAEs are used to
generate new molecular fingerprints with the desired molecular
"At Insilico Medicine we want to be the supplier of meaningful,
high-value drug leads in many disease areas with high probability of
passing the Phase I/II clinical trials. While this publication is a
proof of concept and only generates the molecular fingerprints with the
very basic molecular properties, internally we can now generate entire
molecular structures according to a large number of parameters. These
structures can be fed into our multi-modal drug discovery pipeline,
which predicts therapeutic class, efficacy, side effects and many other
parameters. Imagine an intelligent system, which one can instruct to
produce a set of molecules with specified properties that kill certain
cancer cells at a specified dose in a specific subset of the patient
population, then predict the age-adjusted and specific
biomarker-adjusted efficacy, predict the adverse effects and evaluate
the probability of passing the human clinical trials. This is our big
vision," said Alex Zhavoronkov, CEO of Insilico Medicine, Inc.
Previously, Insilico Medicine demonstrated the predictive power of
its discovery systems in the nutraceutical industry. In 2017 Life
Extension will launch a range of natural products developed using
Insilico Medicine's discovery pipelines. Earlier this year the
pharmaceutical artificial intelligence division of Insilico Medicine
published several seminal proof of concept papers demonstrating the
applications of deep learning to drug discovery, biomarker development
and aging research.
Recently the authors published a tool in Nature Communications
which is used for dimensionality reduction in transcriptomic data for
training deep neural networks (DNNs). The paper published in Molecular Pharmaceutics
demonstrating the applications of deep neural networks for predicting
the therapeutic class of the molecule using the transcriptional response
data received the American Chemical Society Editors' Choice Award.
Another paper demonstrating the ability to predict the chronological age
of the patient using a simple blood test, published in Aging
, became the second most popular paper in the journal's history.
"Generative AAE is a radically new way to discover drugs according
to the required parameters. At Pharma.AI we have a comprehensive drug
discovery pipeline with reasonably accurate predictors of efficacy and
adverse effects that work on the structural data and transcriptional
response data and utilize the advanced signaling pathway activation
analysis and deep learning. We use this pipeline to uncover the
prospective uses of molecules, where these types of data are available.
But the generative models allow us to generate completely new molecular
structures that can be run through our pipelines and then tested in
vitro and in vivo. And while it is too early to make ostentatious claims
before our predictions are validated in vivo, it is clear that
generative adversarial networks coupled with the more traditional deep
learning tools and biomarkers are likely to transform the way drugs are
discovered," said Alex Aliper, president, European R&D at the
Pharma.AI group of Insilico Medicine.
Recent advances in deep learning and specifically in generative
adversarial networks have demonstrated surprising results in generating
new images and videos upon request, even when using natural language as
input. In this study the group developed a seven-layer AAE architecture with
the latent middle layer serving as a discriminator. As an input and
output AAE uses a vector of binary fingerprints and concentration of the
In the latent layer the group introduced a neuron responsible
for tumor growth inhibition index, which when negative it indicates the
reduction in the number of tumour cells after the treatment. To train
AAE, the authors used the NCI-60 cell line assay data for 6252 compounds
profiled on MCF-7 cell line. The output of the AAE was used to screen
72 million compounds in PubChem and select candidate molecules with
potential anti-cancer properties.
"I am very happy to work alongside the Pharma.AI scientists at
Insilico Medicine on getting the GANs to generate meaningful leads in
cancer and, most importantly, age-related diseases and aging itself.
This is humanity's most pressing cause and everyone in machine learning
and data science should be contributing. The pipelines these guys are
developing will play a transformative role in the pharmaceutical
industry and in extending human longevity and we will continue our
collaboration and invite other scientists to follow this path," said
Artur Kadurin, the head of the segmentation group at Mail.Ru, one of the
largest IT companies in Eastern Europe and the first author on the