Welcome to the Machine
By: The Engine
Tags: Entrepreneurship, Artificial Intelligence, MIT, Computer Science, Startup

Depending on whom you talk to, the number of possible chemicals in the world are anywhere from 1060 to 1080 — somewhere, in other words, between the number of atoms in our solar system and the number of atoms in the visible universe. Any one of those chemicals could hold the key to the development of a new drug or other therapy. While human researchers often have a good intuition of where to explore, the sheer vastness of the space leaves ample room for AI to assist in discovering brand-new possibilities that humans might not have even considered.

“In drug discovery, you’re going to have all of these ideas of what could be a good candidate molecule,” says Connor Coley, an assistant professor of chemical engineering at MIT. “You know what could have good bioactivity, good potency, good solubility, and other properties, — but in order to test it, you actually have to make it.” Creating drugs can be a complicated process, requiring multiple chemical reactions in a specific sequence and conditions, done at scale in order to synthesize the desired amount of drug to test.

Coley previously worked in the laboratory of Klavs Jensen, an MIT professor applying those principles to the synthesis and discovery of small molecules, which form the basis of many medicines. Using supervised learning techniques, he and his colleagues have used AI to simulate the testing of candidate molecules, varying catalysts, temperature, and concentration and predicting likely outcomes. “All of those variables will have some effect on the yield, efficiency, and cost of running that reaction,” says Coley. “These adaptive algorithms help you design experiments and tell you what are the most useful experiments to run.”

More recently, Coley and his colleagues have been using supervised machine learning on a huge corpus of chemical reactions collated by Elsevier and other companies to help identify new molecules to test. “We’re taking information chemists have been publishing for the last couple hundred years, and then trying to apply that to new molecules,” Coley says. “Then we put out a full synthetic recipe about how you might be able to make it — everything you should buy, how you should combine them, and what steps you should take to produce the molecule of interest.” More recently, Coley and his colleagues, including Jensen, have worked on creating a robotic platform3 similar to Aspuru-Guziks’ self-driving laboratory to at least partially automate that process. “The idea is to make the research and development process a little bit easier and a little bit faster.”

Coley is a member of MIT’s Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, a group that includes both MIT researchers and representatives from 14 pharmaceutical and biotech companies to help better train algorithms to discover and create new drugs. The companies provide financial support as well as feedback from their own processes to the labs, which produce models and algorithms for the use of members. “We benefit tremendously from understanding what are the real pain points in their workflows, and how well the existing solutions work to alleviate those pain points,” Coley says. In return, such techniques could help companies to quantitatively determine which candidates for drug discovery they should pursue for testing.

Another consortium member, MIT computer science professor Regina Barzilay, recently led the use of deep learning techniques to discover a new antibiotic. Historically, antibiotics have been discovered by screening vast numbers of soil-dwelling microbes, or creating synthetic varieties based on those discoveries that she, James Collins (a Professor of Medical Engineering and Science at MIT), and colleagues wrote about in a paper published in Cell in February 20204. As the number of new molecules have shrunk, however, pharmaceutical companies have turned to screening large chemical libraries in search of novel drugs. Barzilay’s team trained a specialized deep neural network called a variational autoencoder on more than 2,000 molecules to determine what molecular features inhibited the growth of E.coli bacteria, then applied their model to libraries with more than 100 million chemicals, ranking the results.

This process resulted in the identification of a drug with the unwieldy name c-Jun N-terminal kinase inhibitor SU3327, which the researchers renamed Halicin. Not only was it effective against E.coli, but they also found that it inhibited other pathogens, including A. baumanniias, a bacterium that has developed antibiotic resistance and is one of the World Health Organization’s top pathogens to target. In addition, they discovered two other antibiotics effective against antibiotic-resistant E.coli. “The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches,” Barzilay told MIT News5. “This work highlights the significant impact that machine learning can have on early antibiotic discovery efforts by simultaneously increasing the accuracy rate of lead compound identification and decreasing the cost of screening efforts,” the researchers wrote.

Another veteran of Jensen’s lab, Dale Thomas, has spun out a company to synthesize molecules for commercialization. Called MyTide, the startup focuses on peptides, short chains of amino acids including insulin and other hormones that drive reactions inside the body. Among other uses, they can help in regulating obesity, developing vaccines, and targeting cancer though personalized peptide therapeutics. “You can develop a peptide specifically tuned to cancer within a patient’s body and trigger an immune response that allows the body to recognize a tumor as something that is not natural.”

(L-R) Connor Coley, Assistant Professor of Chemical Engineering at MIT; Regina Barzilay, Delta Electronics professor in the Department of Electrical Engineering at MIT; Dale Thomas, Co-founder of mytide therapeutics; Finale Doshi-Velez, John L. Loeb associate professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences

With 20 different amino acids to choose from, however, the number of potential peptides that could be used in therapies is overwhelming. “There are over a trillion different peptides that can be manufactured — and that’s an understatement,” Thomas says. In addition, each amide bond between amino acids is unique, requiring different conditions to form, and with dangerous side reactions if you get it wrong. “You have to understand not only the conditions for linking one amino acid to another, which is 400 different types of reactions, but also how previous couplings affect the current coupling. If you do not have the correct data collection and ability to use in-line analytics, you don’t have the ability to capture the data of this building of peptides.”

In order to solve the problem, the company has built a synthesizing machine that can move amino acids and other reagents through a reactor at the rate of 3,000 reactions a day. As the reactions occur, the system collects second-by-second data, including traces of UV light absorbed at different wavelengths, to show the extent of coupling of molecules. “That enables us to leverage machine learning, because we are building high-quality datasets that feed into machine learning models,” says Thomas. Using the output from those models, the machine operators can then adjust variables such as the type of reagents, reaction time, and temperature, to optimize molecule synthesis, as well as to create new molecules for new purposes.

“After we run 5,000 molecules through this platform, we have 150,000 data points on the output. And we can feed that back in, in this closed loop manner, in order to redefine the next set of peptides that we should manufacture, and then carry through again to find the right target.” Using these techniques, the company has been able to reduce the time required to synthesize non-naturally occurring peptides from nine weeks to four days — and use fewer reagents overall. “Our company’s real aim is to start building in more complex chemistries than have previously been able to be manufactured,” Thomas says. For example, the company is experimenting with building using D-amino acids, mirror images of naturally occurring L-amino acids that show increased bioactivity. Other projects include adding polymer side chains to peptides to create cardiac medicines and oils to treat eczema.

In addition to drug development, researchers are using AI to create other new therapies as well. Cellino, one of The Engine’s portfolio companies, is developing cell and tissue therapies by guiding the development of stem cells using lasers. Induced pluripotent stem cells (iPSCs) are grown on a well plate with a 20-nanometer undercoating of titanium. A laser shot at the bottom of the plate can target an individual cell, poking a tiny bubble in the cell membrane through which a liquid-borne cargo of growth factors or other components can enter, or — if the bubble is big enough — killing the cell outright. Using these controls, company scientists can precisely control the differentiation of cells into therapeutic cell types to treat various disorders.

“We are positioning ourselves as a cell and tissue foundry,” says Marinna Madrid, cofounder of Cellino. “But what’s unique about our technology is that we’re able to do that with single-cell control because of the laser-based technology we use.” The company hopes to partner with pharma and biotech companies that are differentiating cells for therapeutic applications but are having difficulty in achieving uniform, high-quality cells for implantation.

One therapy the company has been working on, for example, is the implantation of retinal pigment epithelial cells in order to treat age-related macular degeneration (AMD). As it grows these cells, the company has worked with biologists to identify which cells or regions of tissue to keep or enhance, killing the others with the laser. “There are a few things that correlate with the cells being good at doing their job in the body,” says chief science officer Arnaldo Pereira, “for example, the amount of melanin granules in them, or how easily they can phagocytose the garbage that the photoreceptors sitting atop them slough off.”

(Left) Marinna Madrid, Co-founder of Cellino (Right) Arnaldo Pereira, Principal Machine Learning

Engineer at Cellino

Ordinarily, it would take a Ph.D-level biologist painstaking hours with a microscope to identify these cells. Using AI, however, Pereira and his team have been able to design an algorithm on microscope images to quickly identify which cells to save. “If you can remove the underperforming cells and leave space for good cells to grow in that space, then you can increase your yield, and also increase the variability of cells that you get in the end,” Pereira says. The company is working on a way to totally automate the process, including cell detection and lysing with the laser in order to streamline the process.

The same technique could be used for other cell therapies, for example, transplanting dopaminergic neurons to treat Parkinson’s disease. Some studies have shown that transplant-ing such cells at an earlier progenitor stage produces better outcomes, but it can be difficult to determine at that stage which cells will succeed in fully differentiating into mature cells. “We plan to develop machine learning algorithms that can predict at earlier and earlier times whether or not the cell is going to be functional down the road,” Madrid says.

In addition to just killing under-performing cells, the process is also able to manipulate cells by controlling the strength of the laser to poke just a small hole, through which liquid-borne cargo can enter, without killing the cell. The process could be used to insert growth factors or gene modulators to turn on or off genes within cells to individually control their growth with a high degree of precision. “So if you see that a cell is starting to differentiate down an incorrect pathway, but it’s not completely lost, you could deliver a gene modulator into that cell to turn the right genes on or off to kind of course correct,” Madrid says.

How AI is Personalizing Medicine

Doctors are already starting to use AI in limited ways as a virtual medical assistant with special training for pattern recognition. Radiology has been an early pioneer in this area, using AI’s ability to recognize complex patterns in images.6 The FDA has already approved more than 50 algorithms7 for use with AI platforms to help detect early signs of lung, breast, and brain cancer. In Uganda, an AI Lab at Makerere University uses a smartphone app and a microscope to detect malaria in blood samples, compensating for a lack of trained pathologists in the country.

In the coming years, however, AI has the potential to transform every aspect of clinical diagnosis and treatment. “AI is not the decision-maker,” says Finale Doshi-Velez, assistant professor of computer science at Harvard, who has pioneered such efforts, “but it can be the tool that provides the information and potentially the recommendations or anti-recommendations based on a patient’s history to help doctors make decisions under conditions of uncertainty.”

With new legislation on Electronic Medical Records (EMR) under consideration to allow patients better share their medical information digitally, new apps could help doctors sift through a patient’s histories quickly in order to arrive at more effective diagnoses and treatments. “You have a lot of data about the patient in the health record but the doctor just doesn’t have the time to look at it,” says Doshi-Velez. An app could flag, for example, when a cardiac medication a doctor prescribes might produce unforeseen side-effects. “Errors may get made simply because the doctor didn’t know enough about the patient.

Doshi-Velez is currently working on more advanced algorithms to personalize treatment modalities for treating diseases such as HIV, which relies on complex cocktails of drugs given in individualized combinations, dosages, and sequences. Using data on efficacy in past patients, an algorithm could analyze a new patient based on the patient’s history, including resistance mutations in the virus and previously prescribed drugs, to recommend an optimal cocktail for that patient. “The trick is to look at the data and then propose alternate strategies that have not been tested, and be able to assess how well you think your new proposal will do,” says Doshi-Velez.

Not all conditions may require such highly individualized treatments, however. Doshi-Velez’ lab is also working with algorithms to produce more generalized treatment protocols using past data to sort patients into categories based on the symptoms they present. “My hunch is that a lot of times that stratification is going to get us a long way; if you could split people among the right variables, then you could put them into one of 300 bins, or 10 bins,” Doshi-Velez says. Once the AI has helped create those bins, she says, then humans could help determine which treatment bins patients fall into. In an ICU setting, AI could even develop protocols for treatment that might fit on just a few pieces of paper, differentiating patients based on temperature, blood pressure, and other symptoms. “They could say, whenever you’re making this decision ask a patient about these five variables and follow the flow chart.”