Collaborating Authors


Atomwise raises $123 million to accelerate drug discovery with AI


Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startup to scale its technology and team as it expands its portfolio of joint ventures with researchers at the University of Toronto, Duke University School of Medicine, Charles River, Bayer, Eli Lilly, Merck, and others. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America. Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years.

Artificial intelligence attacks the coronavirus


MONTREAL – A research project located at the intersection between genomics, artificial intelligence and medicinal chemistry will attempt to accelerate the identification of new molecules that could prove useful in the fight against the coronavirus. The project brings together Génome Québec, the Institute for Research in Immunology and Cancer (IRIC) of the University of Montreal, the University of Montreal, Mila – Quebec Institute of Artificial Intelligence and McMaster University. "When you start from scratch with traditional methods, developing a new drug can take ten or fifteen years," said Professor Michael Tyers of IRIC / University of Montreal. We hope that this approach will allow us to go considerably faster. Each discipline will go there with a contribution of its own.

How Artificial Intelligence Innovations Induced Industrywide Advancements?


Moreover, across the healthcare and pharmaceutical industry, technology is making great advancements. Living under the reign of terror induced by a coronavirus, no other generation than us can understand the true benefits of technology in reshaping the healthcare industry. Today AI and its subsets are being used extensively for drug discovery and medical treatment planning. However, the upsurge of AI in this industry has been noted in 2019 when a Hong Kong biotech startup called InSilico Medicine partnered with the University of Toronto researchers to create a drug in order to advance the concept to initial testing. As noted by Fortune, the significance of AI on pre-clinical development and on the economics of healthcare is worth watching.

Visualizing AI startups in drug discovery


As a machine learning researcher in the biology field, I have been keeping an eye on the recently emerging field of AI in drug discovery. Living in Toronto myself, where many "star" companies in this field were founded (Atomwise, BenchSci, Cyclica, Deep Genomics, ProteinQure… just to name a few!), I talked to many people in this field, and attended a few meetup events about this topic. What I learned is that this field is growing at such a rapid speed, and it is becoming increasing hard to keep track of all companies in this field and get a comprehensive view of them. Therefore, I decide to use my data science skills to track and analyze the companies in this field, and build an interactive dashboard ( to visualize some key insights from my analysis. The Chief Strategy Officer of BenchSci (one of the "star" AI-drug startups in Toronto), Simon Smith, is an excellent observer and communicator in the AI-drug discovery field.

AI is reinventing the way we invent


Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.

Yoshua Bengio becomes scientific advisor of Perceiv AI - Mila


Yoshua Bengio, Founder of Mila and computer science professor at University of Montreal, will support the ongoing research of Perceiv AI in precision medicine to improve and optimize drug development clinical trials. Founded by graduate students out of University of Montreal and Mila, Perceiv AI aims to improve treatment efficacy thanks to refined patient selection. Through advanced Machine Learning algorithms, Perceiv AI helps pharmaceutical companies with more efficient and accurate subject stratification for their clinical trials. Heterogeneity in patient populations creates challenges in enrolment for clinical trials, which can result in increased trial costs and failures, delaying the commercialization of much-needed treatments. "For having seen the ravages of diseases like Alzheimer's from up close, I am very motivated to see more development of AI techniques, such as done at Perceiv AI, to provide better targeted treatments, and I am delighted to see the next generation of AI researchers embarking on such projects of important value for society while contributing to grow the startup ecosystem in Montreal," said Yoshua Bengio, Ph.D. "We are thrilled to reinforce our relationship with Mila and to welcome Yoshua as an advisor!" said Christian Dansereau, Ph.D., CEO and co-founder of Perceiv AI. "With their help, we will be able to leverage the most recent advances in Representation Learning to further refine our prognostic biomarkers, not only for Alzheimer's but also for new therapeutic areas."

New Advancements in AI for Clinical Use


Naheed Kurji is the President and CEO of Cyclica, a Toronto-based biotechnology company that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Cyclica leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Can you discuss in what way AI is used in this process? Technology has played a critical role in drug discovery dating back to the '80s. However, the drug discovery and development process is still very inefficient, time consuming and expensive, costing more than 2 billion dollars over 12 years.

Abolish the #TechToPrisonPipeline


The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release.[38] At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.

Massive Growth Of Global Lab Automation Industry 2020:Booming Worldwide Top Key Players Perkinelmer, Inc., Danaher Corporation, Thermo Fisher Scientific, Inc., Agilent Technologies, Inc – 3w Market News Reports


By Equipment the market for lab automation is segmented into automated liquid handlers, automated plate handlers, robotic arm, automated storage and retrieval systems. By software the lab automation market is segmented into laboratory information management system, laboratory information system, chromatography data system, electronic lab notebook, scientific data management system. On the basis of analyzer the market is segmented into biochemistry analyzers, immuno-based analyzers, hematology analyzers segments. By application the segmentation of the market is drug discovery, genomics, proteomics, protein engineering, bio analysis, analytical chemistry, system biology, clinical diagnostics, lyophilization. Based on end user the lab automation market is segmented into biotechnology & pharmaceuticals, hospitals, research institutions, academics, private labs. On the basis of geography, lab automation market report covers data points for 28 countries across multiple geographies such as North America & South America, Europe, Asia-Pacific, and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2017, North America is expected to dominate the market.

DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach Machine Learning

The objective of this study is to predict suicidal and non-suicidal deaths from DNA methylation data using a modern machine learning algorithm. We used support vector machines to classify existing secondary data consisting of normalized values of methylated DNA probe intensities from tissues of two cortical brain regions to distinguish suicide cases from control cases. Before classification, we employed Principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimension of the data. In comparison to PCA, the modern data visualization method t-SNE performs better in dimensionality reduction. t-SNE accounts for the possible non-linear patterns in low-dimensional data. We applied four-fold cross-validation in which the resulting output from t-SNE was used as training data for the Support Vector Machine (SVM). Despite the use of cross-validation, the nominally perfect prediction of suicidal deaths for BA11 data suggests possible over-fitting of the model. The study also may have suffered from 'spectrum bias' since the individuals were only studied from two extreme scenarios. This research constitutes a baseline study for classifying suicidal and non-suicidal deaths from DNA methylation data. Future studies with larger sample size, while possibly incorporating methylation data from living individuals, may reduce the bias and improve the accuracy of the results.