Connect with us

Healthcare

AI Used To Create Drug Molecule That Could Fight Fibrosis

mm

Published

 on

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.

Spread the love

Deep Learning Specialization on Coursera

Blogger and programmer with specialties in machine learning and deep learning topics. Daniel hopes to help others use the power of AI for social good.

Healthcare

London-Based Startup LabGenius Raises $10M

Published

on

London-Based Startup LabGenius Raises $10M

The London-based startup LabGenius announced that they raised over $10 million in Series A Funding. They are a drug discovery company that utilizes artificial intelligence (AI), robotic automation, and synthetic biology. Their main focus is to find novel protein therapeutics. 

According to Dr James Field, CEO and Founder of LabGenius, “Protein therapeutics have an unparalleled potential to both treat disease and alleviate human suffering. By transforming how these drugs are discovered, we have a shot at improving the lives of countless people. Being able to robustly engineer novel therapeutic proteins has immense commercial and societal value. The discovery of protein therapeutics has historically been highly artisanal, relying heavily on humans for both experimental design and execution. This dependence has proved limiting because, as a species, we’re cognitively incapable of fully grasping the complexity of biological systems.”

The Series A investment round is led by Lux Capital and Obvious Ventures. Other participants included Felicis Ventures, Inovia Capital, Air Street Capital, and other existing investors. CEO and founder of Recursion Pharmaceuticals, Chris Gibson, along with Inovia Capital General Partner Patrick Pichette, are also investing. Pichette is the former CFO of Google. 

According to the company, they will use the capital to “scale their team, expand the scope of its discovery platform, and initiate an internal asset development program.” One of their main goals is to evolve novel antibody fragments. These could be used to treat certain conditions that can’t rely on conventional antibody formats. 

Lux Capital and Obvious Ventures

Zavain Dar, Partner at Lux Capital, along with Nan Li, Managing Director at Obvious Ventures, have been involved in the life science startup environment for some time. Their investment strategy dates back nine years, including a 2013 investment into Zymergen, a molecule discovery and manufacture company based out of California. In 2016, they were involved in Recursion Pharmaceuticals, who went on to a series C raise of $156 million in July. 

Their strategy follows a path, starting at industrial biotech technology with Zymergen and followed by root-cause biology discovery with Recursion Pharmaceuticals. It is closed out by creating composition of matter and IP with LabGenius.  

Dar explained his reasoning behind choosing LabGenius over other startups. 

“We scoured the globe, and didn’t want to be constrained by what happened to be in our backyard,” he says. “They are leading the pack…and now with backing and pharma partnerships, should be in a good position.”

Humans No Longer Sole Agents of Innovation

When speaking to TechCrunch, Dr James Field said, “My central thesis, the thing that’s really the driving force behind the company, is the conviction that we’re entering an age in which humans will no longer be the sole agents of innovation. Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines. An empirical computation engine is an artificial system capable of recursively and intelligently searching a solution space.”

The company has created a discovery platform called EVA, and it integrates multiple new technologies coming from different fields including artificial intelligence. After discovery and characterisation, LabGenius then sends their proprietary molecules to clinics. 

Field explains the company’s EVA technology as a “machine learning-driven, robotic platform”,” that is capable of “designing, conducting and critically learning from its own experiments.” 

“For decades, scientists, engineers and technologists have dreamt of building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies and sophisticated real-world products,” says Field.

“For protein engineers, that dream has now entered the realm of possibility. The rapid pace of technological development across the fields of synthetic biology, robotic automation and ML has given us access to all the essential ingredients required to create a smart robotic platform capable of intelligently discovering novel therapeutic proteins.”

 

Spread the love

Deep Learning Specialization on Coursera
Continue Reading

Healthcare

Brain Cancer Detected By AI Analyzing Blood Test Results

mm

Published

on

Brain Cancer Detected By AI Analyzing Blood Test Results

Recently, researchers associated with the University of Strathclyde, Glasgow patented a method of analyzing blood samples to detect brain cancer. The researchers at ClinSpec Diagnostics Limited combined spectroscopy and AI algorithms to detect brain cancer based on blood biopsies. As reported by Psychology Today, The research was recently published in the journal Nature Communications, and according to the research team, the work represents a significant development in the utilization of clinical spectroscopy and AI.

The research presented in the study could make catching brain cancer much easier and simpler. Frequently occurring headaches may be a symptom of brain cancer, but even though headaches are very common, brain cancer is not. Clinicians need a better method of discerning which headaches are causes for concern and which are more benign. Doctors must be able to carry out some form of triage and reduce the amount of time and resources invested in diagnosing brain cancer with costly brain imaging scans. If a simple blood test could give clinicians reliable information that could help them diagnose cases of brain cancer, lives could be saved.

It was for this reason that the ClinSpec researchers aimed to develop an algorithm that would help doctors sort through the cases of possible brain cancer patients, distinguishing them from other causes of headaches.

One of the common methods of detecting diseases like cancer is liquid biopsy, doing biopsy on fluids of the body instead of tissue samples. The liquid biopsy market is swiftly growing, hitting an estimated $2.4 billion dollars in size according to market research from BC Research LLC. Liquid biopsy proves effective at detecting signs of cancer, as it is able to detect cell-free circulating tumor DNA, or ctDNA, and circulating tumor cells, or CRCs. However, the researchers from ClinSpec utilized a different method of analysis, doing spectroscopy on blood samples to find biochemical markers indicative of cancer.

Spectroscopy is the process of using electromagnetic radiation to find certain targeted chemical components. Light is split up into component electromagnetic frequencies, and these frequencies will react differently with different chemicals. The ClinSpec research team used infrared light to create representations of blood samples, a technique dubbed attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The research team stated that the technique is a non-destructive, non-invasive technique that reliably creates a biochemical profile of a sample without the need to prepare the sample extensively. The representations of the blood samples could then be analyzed for aberrations, checked for possible signs of cancer.

In order to analyze the data, a support vector machine was used to create a classification model. Support vector machines are used for classification and regression analysis, and they operate by drawing decision boundaries, or lines that separate a dataset into multiple classes. The algorithm tries to maximize the distance between the dividing line and the data points on either side of the line, and the greater the distance the more confident the classifier is.

The research team stated that their method of analysis for the blood samples was able to effectively distinguish cancer samples from non-cancer samples. There was a sensitivity rate of 93.2% and a specificity rate of 92.8%. According to MDDI Online, The researchers report that when analyzing samples from a group of 104 different patients, their AI-assisted method was able to distinguish healthy patients from cancer around 86% of the time.

The researchers explained in the study:

“This work presents a step in the translation of ATR-FTIR spectroscopy into the clinic. This step towards high-throughput analysis has implications in the field of IR spectroscopy as well as the clinical environment. Analysis of blood serum using this technique would fit ideally in the clinical pathway as a triage tool for brain cancer.”

Spread the love

Deep Learning Specialization on Coursera
Continue Reading

Deep Learning

Foodvisor App Uses Deep Learning to Monitor & Maintain Your Diet

mm

Published

on

Foodvisor App Uses Deep Learning to Monitor & Maintain Your Diet

Foodvisor, a startup that launched its new AI-based app in France in 2018 is about to change the manner in which you track and keep your diet plans. As TechCrunch explains, the Foodvisor app “helps you log everything you eat in order to lose weight, follow a diet or get healthier.” The users are also given the ability to input additional data by capturing a photo of the food you are about to eat.

The app works by using deep learningto enable image recognition to detect what you’re about to eat. In addition to identifying the type of food, the app tries to estimate the weight of each item.” Using autofocus data, it also makes an evaluation of the distance between the plate of food and the phone it is on.

Foodvisor also allows its users to manually correct any data before the meal is logged in. For many people tracking their diet nutrition trackers turn out to be too demanding, and the idea behind Foodvisor is to make “the data entry process as seamless as possible.”

Finally, it produces a list of nutrition facts about what has just been consumed –  calories, proteins, carbs, fats, fibers, and other essential information. The users can then set their own goals, log their nutritional activities and monitor their progress.

Foodvisor App Uses Deep Learning to Monitor & Maintain Your Diet

The app itself is free to use, but it also offers a premium subscription that varies between $5 and $10. These subscriptions offer more analysis and diet plans, but the main feature  of these plans being “that you can chat with a registered dietitian/nutritionist directly in the app.”

So far, Foodvisor was able to gather 1.8 million downloads and is available on IOS and Android systems in French, English, German and Spanish, and has raised $1.5 million so far (€1.4 million). Co-founder and CMO Aurore Tran says the company has “enriched [its] database to better target the American market.”

The trend of using AI systems in food apps was started back in 2015 when Google started developing its Im2Calories,  a system that counted calories based on Instagram photos. It was followed, as The Daily Meal reported, “researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and the Qatar Computing Research Institute created Pic2Recipe, an app that uses artificial intelligence to predict ingredients and suggests similar recipes based on looking at a picture of food.”

The same team is still trying to “improve the system to understand images of food in more detail, including identifying cooking and preparation methods. They are also interested in recommending recipes based on dietary preferences and available ingredients.”

But as Ai capabilities develop, it seems that Foodvisor took the idea one step further.

 

Spread the love

Deep Learning Specialization on Coursera
Continue Reading