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AI Used To Create Drug Molecule That Could Fight Fibrosis

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AI Used To Create Drug Molecule That Could Fight Fibrosis

Creating new medical drugs is a complex process that can take years of research and billions of dollars. Yet it’s also an important investment to make for people’s health. Artificial intelligence could potentially make the discovery of new drugs easier and substantially quicker if the recent work of the startup Insilico Medicine continues to make progress. As reported by SingularityHub, the AI startup has recently utilized AI to design a molecule that could combat fibrosis.

Given how complex and time-consuming the process of discovering new molecules for a drug is, scientists and engineers are constantly looking for ways to expedite it. The idea of using computers to help discover new drugs is nothing new, as the concept has existed for decades. However, progress on this front has been slow, with engineers struggling to find the right algorithms for drug creation.

Deep learning has started to make AI-driven drug discovery more viable, with pharmaceutical companies investing heavily in AI startups over the past few years. One company has managed to use AI to design a molecule that could combat fibrosis, taking only 46 days to do dream up a molecule resembling therapeutic drugs. Insilco Medicine combined two different deep learning techniques to achieve this result: reinforcement learning and generative adversarial networks (GANs).

Reinforcement learning is a machine learning method that encourages the machine learning model to make certain decisions by providing the network with feedback that elicits certain responses. The model can be punished for making undesirable choices or rewarded for making desirable choices. By using a combination of both negative and positive reinforcement the model is guided toward making desired decisions, and it will trend towards making decisions that minimize punishment and maximize reward.

Meanwhile, generative adversarial networks are “adversarial” because they consist of two different neural networks pitted against one another. The two networks are given examples of objects to train on, frequently images. The job of one network is to create a counterfeit object, something sufficiently similar to the real object that it can be confused for the genuine article. The job of the second network is to detect counterfeit objects. The two networks try to outperform the other network, and as they are both increasing their performance to overcome the other network, this virtual arms race leads to the counterfeit model generating objects that are nearly indistinguishable from the real thing.

By combining both GANS and reinforcement learning algorithms, the researchers were able to have their models produce new drug molecules extremely similar to already existing therapeutic drugs.

The results of Insilico Medicine’s experiments with AI drug discovery were recently published in the journal Nature Biotechnology. In the paper, the researchers discuss how the deep learning models were trained. The researchers took representations of molecules already used in drugs to handle proteins involved in idiopathic pulmonary fibrosis or IPF. These molecules were used as the basis for training and the combined models were able to generate around 30,000 possible drug molecules.

The researchers then sorted through the 30000 candidate molecules and selected the six most promising molecules for lab testing. These six finalists were synthesized in the lab and used in a series of tests that tracked their ability to target the IPF protein. One molecule, in particular, seemed promising, as it delivered the kind of results that are desired in a medical drug.

It’s important to note that the fibrosis drug targeted in the experiment has already been extensively researched, with multiple effective drugs already existing for it. The researchers could reference these drugs, and this gave the research team a leg up as they had a substantial amount of data to train their models on. This doesn’t hold true for many other diseases, and as a result, there is a larger gap to close on these treatments.

Another important fact is that the company’s current drug development model only deals with the initial discovery process,and that the molecules generated by their model will still require many tweaks and optimizations before the molecules could potentially be used for clinical trials.

According to Wired, Insilico Medicine’s CEO Alex Zharvornokov acknowledges that their AI-driven drug isn’t ready for field use, with the current study just being a proof of concept. The goal of this experiment was to see how quickly a drug could be designed with the assistance of AI systems. However, Zhavornokov notes that the researchers were able to design a potentially useful molecule much faster than they could have if they had used regular drug discovery methods.

Despite the caveats, Insilico Medicine’s research still represents a notable advancement in the usage of AI to create new drugs. The refinement of the techniques used in the study could substantially shorten the amount of time required to develop a new drug. This could prove especially useful in an era where antibiotic-resistant bacteria are proliferating and many previously effective drugs losing their potency.

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How Governments Have Used AI to Fight COVID-19

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How Governments Have Used AI to Fight COVID-19

Governments all around the globe are using artificial intelligence (AI) to help fight against the ongoing COVID-19 pandemic. The technology is being used for various different things, including speeding up the development of testing kits and treatments, giving citizens access to real-time data, and tracking the spread of the virus. 

Here are some of the different countries. 

South Korea

South Korea’s government, one that is being touted as an example for how to combat the virus, pushed their private sector to start developing testing kits right away, immediately after the reports began to arrive out of China. 

One of those companies was Seoul-based molecular biotech company Seegene, which used AI to help quicken the process of developing testing kits. The company was able to submit its solution to the Korea Centers for Disease Control and Prevention (KCDC) just three weeks after the scientists began their work. According to Chun Jong-Yoon, founder and chief executive of the company, the process would have taken at least two to three months without the use of AI. 

Since the testing kits are such a crucial part of getting the virus under control, this proves that AI technology can play a huge role in the fight.

Traditionally, the approval process for new medical equipment, including testing kits, takes around 18 months. The KCDC decided to move the process ahead and approved the tests in one week. The government’s own patient samples were able to be used for evaluation. 

Telecoms firm KT has also partnered with South Korea government ministries in order to use AI-based healthcare services to track the spread of the virus. 

The research project was led by the ICT Ministry and Ministry of Interior and Safety, along with universities and research institutes. KT will be responsible for providing mobile data, who can help create maps. These maps can then provide insight into how the populations are moving and the virus spreading.

China

Scientists in China used AI in order to speed up scientific processes. Through the use of the technology, they were able to recreate the genome sequence of the virus in a month. Comparing that to the months it took scientists to create the sequence of the SARS virus in 2003, it is a big step up. 

Taiwan

In Taiwan, the technology has been used as well. Audrey Tang, Taiwan’s digital minister, relied on AI to develop real-time digital updates. These updates could alert citizens of certain hazardous locations, where infections had been previously detected. They were also able to use it to create a live map of local face mask supplies. 

United States

In the United States, the White House Office of Science and Technology Policy called for the development of the COVID-19 Open Research Dataset (CORD-19). This dataset is a collection of thousands of different machine-readable COVID-19 literature. There are over 44,000 scholarly articles that can be used by the research community. 

“It’s all-hands on deck as we face the COVID-19 pandemic,” Microsoft’s chief scientific officer, Dr. Eric Horvitz said. “We need to come together as companies, governments, and scientists and work to bring our best technologies to bear across biomedicine, epidemiology, AI, and other sciences. The COVID-19 literature resource and challenge will stimulate efforts that can accelerate the path to solutions on COVID-19.”

While there is still a lot more that can be accomplished with artificial intelligence (AI), these are some of the current examples from around the globe. If governments are convinced by the results, the use of AI during a pandemic could become one the first defense options in the future.

 

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Anton Dolgikh, Head of AI, Healthcare and Life Sciences at DataArt – Interview Series

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Anton Dolgikh, Head of AI, Healthcare and Life Sciences at DataArt - Interview Series

Anton Dolgikh leads AI and ML-oriented projects in the Healthcare and Life Sciences practice at DataArt and runs educational and training developers focused on solving business problems with ML methods. Prior to working at DataArt, Dolgikh worked in the Department of Complex Systems at the Université Libre de Bruxelles, a leading Belgian private research university.

What was it that originally inspired you to pursue AI and life sciences as a career?

A passion for searching the links between phenomena and facts. I always like to read. I love books. At university, I discovered a new source of information – articles. At some point, it appeared that to get a complete picture, to crystallize the beautiful truth from a mass of information is almost impossible. And here comes AI. Statistics, machine learning of course, and natural science with AI at the top all act to build the bridge between the human brain’s thirst for knowledge and a world where all the laws are known and there are no black boxes.

 

You currently educate and train developers who are focused on solving business problems with ML methods. Is there a specific field of machine learning that you focus on more, for example deep learning?

Yes, deep learning is a very popular and, let’s be honest, powerful instrument; we cannot neglect it. I personally prefer the Bayesian interpretation of classical algorithms, or even a combination of neural networks and a Bayesian approach — for example, a Bayesian Variational Autoencoder. But I believe that the most important thing to teach new ML guys is not to blindly use ML machinery like a magic black box,  but rather perceive the basic principles behind each and every method. A must-have skill is the ability to explain the predictions obtained for a business audience.

 

In March 2019, you wrote an article called ‘Are we Ready for Machine Radiologists, and their Mistakes?‘. In the article you outlined the pros and cons of accepting results from machine radiologists versus human radiologists. If you had to choose between a human and a machine giving you results, which would you choose and why?

I prefer a human radiologist. Not because I have some special knowledge that AI tis heavily prone to errors and decisions are intrinsically erroneous. No, it’s more a question of empathy and the psychological nature. I want to support human doctors during this difficult period. Moreover, I believe that in the nearest future, we will only see AI augment human ability.

 

You recently wrote a white paper called ‘The Impact of Artificial Intelligence on Lifespan.’ In this paper, you stated that AI should be viewed as a tool in the search for longer life. What are some of the more promising methodologies that AI could apply to the quest to extend human lifespan?

Today the new tool of AI is beginning to operate in scientific laboratories on par with classical instruments and approaches. This fact itself is promising. AI is here to help, not replace, us in the struggle to cope with the huge quantities of data flooding not only laboratories but even our personal lives.

 

Also discussed in the same white paper is a claim by Biogerontology Research Foundation AI Director and CEO of Insilico Medicine, Dr Alex Zhavoronkov, that increasing lifespan to 150 years is not a fantastic goal. Do you believe that a child born in 2020 will be able to live to 120 or even 150 years?

I want to believe. Being a scientist by education and belief, I have to base my decisions on facts, on understanding the progress of scientific methods in the area. We’ve made an impressive leap in the fields of genetics, biotechnology and medicine in general, and this strengthens my belief. And don’t forget that a substantial part of the success in increasing lifespan is a healthy environment and a healthy lifestyle, so we have to work on this.

 

In this same paper you mention the potential for mind uploading (transhumanism). Do you believe that this could eventually be a reality, and how does it make you feel personally?

I’ve thought a lot about it. Frankly, it makes me feel frustrated. I think that we associate personality with what we see in a mirror, and for me, it’s hard to detach my character from my body. Nevertheless, this doesn’t mean it is not possible. And, yes, I believe that sooner or later mind uploading will become feasible. The consequences are much harder to foresee.

 

You’re currently the Head of AI, Healthcare and Life Sciences at DataArt. What are some of the most interesting projects DataArt is currently working on?

We have a project dedicated to new drug development. It’s inspiring how computational methods have developed to fuel and direct the progress in medicinal chemistry and pharmacology. We also do a lot of work on applying AI to extract information from medical texts such as clinical trial reports, medical articles, and specialized forums. It’s hard work, but it takes us closer to the digitalization of healthcare, and I find this exciting.

 

As an avid book lover, I also need to ask what books you recommend?

I don’t want to call the list below a “must-read”. While there are standard textbooks which are suited for large groups, most of the “must-read” books express personal experience and background of an adviser.

 

Is there anything else that you would like to share about DataArt?

DataArt is an excellent example of the recent trend toward digitalization of almost every aspect of life and activity. This trend increases responsibility in software development because today it’s not only about building a site for a shop, for example, in which case a mistake by the developer will have minimal consequences. Today a developer’s mistake could become a national or worldwide catastrophe if it involves a program controlling the functioning of, for example, a nuclear plant. DataArt’s responsible approach to software development in a broad sense gives me confidence in what we develop, and I am very proud to be part of the company and the work that we are doing.

As for another recent project of hours, last year DataArt launched a prototype application called ‘SkinCareAI’, which analyses skin images to detect early signs of melanoma. Featuring the latest advancements in machine learning (ML) technology, SkinCareAI was developed by DataArt ML expert Andrey Sorokin for the International Skin Imaging Collaboration (ISIC) challenge.

To learn more about some of our other projects and case studies, please go to DataArt’s Healthcare and Life Sciences page.

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AI Discovers Smell Genes Linked To Cancer Outcomes

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AI Discovers Smell Genes Linked To Cancer Outcomes

A team of researchers from Oxford University has recently used AI to discover a potential link between colon cancer and the expression of specific smell-sensing genes. As Phys.org reports, researchers from Oxford University and the University of Zurich have recently, assisted by an AI model, discovered that the expression of specific smell-sensing genes within the colon cancer cells indicates a higher probability of worse outcomes.

Genes are expressed when the information that is found within our DNA is used to make molecules like proteins. Gene expression often controls how many proteins are made and when they are made, acting on/off switches. Human beings have approximately 400 genes responsible for our sense of smell, but rather crucially for the study, the genes are also expressed in other parts of the body, aside from the nose. If these smell genes are expressed, it means that the instructions for these genes are being read and carried out. By making changes to the cells, scientists can manipulate the level to which genes are expressed and used.

The study that was recently published in Molecular Systems Biology was lead by Dr. Heba Sailem of the Insitute of Biomedical Engineering and Oxford University. Sailem and fellow researchers examined how cells in the body are organized, aiming to study how cancer leads to the loss of tissue structure in the body. In order to develop effective therapies, scientists must understand which genes play a role in tissue alteration. The research team employed computer vision algorithms to detect changes within the organization of cell samples. The AI model was given image data collected by robotic microscopy, which contains millions of images of colon cancer cells.

The research team then experimented by reducing the expression of every gene in the individual colon cancer cells. After perturbations were applied to the genes and their expression decreased, the researchers found that smell-sensing genes appear to be strongly correlated with how cells align and spread. It appeared that reducing the expression of smell genes could potentially control the spread of cells by reducing their ability to move. On the other hand, cell motility could be increased by having higher expression levels of the smell genes in question.

Sailem explained that the smell genes are like a “sixth sense” that the cancer cells can use to find their way outside of the tumor environment, which is toxic, and spread to other regions of the patient’s body. Sailem went on to explain just how important AI was in making this discovery. The AI model used by the researchers was able to greatly increase the speed the research was carried out at. The AI model, after being trained on a large database of gene functions and appearances, is able to automate the task of identifying certain types of cells within images. Sailem explained:

“Using the developed AI system, we can now learn much more from these experiments and accelerate the identification of genes that alter the structure of tissues in cancer.

CRIPSR (Clustered Regularly Interspaced Short Palindromic Repeats), the gene-editing technology, is the primary way that gene expression levels for the approximately 20,000 genes in the cell are reduced to study how gene expression impacts cancer cells. When combined with advances in gene-editing technology, the research done by Sailem and colleagues could enable new methods of identifying the roles different genes play in different types of cancer, which could enable new kinds of therapies.

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