Streamlining drug development using machine learning

InVivo AI is using artificial intelligence to streamline the development of new drugs. We hope to help bring therapies to patients in less time than currently possible by facilitating accurate toxicity screening in the earliest phases of drug discovery.

Daniel Cohen

Co-Founder, CEO

MSc in Comp. Neuroscience, 

McGill University.

Research experience in computational modelling for neuroscientific applications. Ex biopharma VC. Leading business development and bridging the gap between InVivo's scientific and technical teams.

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Our prototype platform integrates molecular, target, and tissue-based descriptors to accurately distinguish between toxic and non-toxic small molecule drugs. Training data has been aggregated across a combination of public, private, and academic datasets, providing us with access to information about thousands of drugs, drug targets, and clinical trial outcomes. Our proof-of-concept approach can accurately forecast the likelihood of chemically-mediated toxicity while also providing insight into a drug's primary pharmacology. This allows us to predict which compounds are likely to have manageable safety margins in clinical testing, in turn allowing us to make predictions about a drug's eventual likelihood of success.

Our vision is to become an integral part of small molecule R&D. By helping to streamline drug development through efficient pipeline optimization, we hope that our platform will save time and money in bringing new therapies to patients. 


The rate of clinical trial failures has risen substantially over the past decade, with almost 40% occurring for toxicity reasons. Drug development programs attempt to mitigate these risks by filtering out toxic molecules in the earliest stages of drug discovery. Despite this, it remains difficult to identify compounds that have unfavorable toxicity profiles before conducting clinical trials. The new reality of pharmaceutical R&D is that a 10% improvement in the ability to forecast clinical trial outcomes preclinically is worth ~$100M per approved drug. 

InVivo AI is therefore developing best-in-class toxicity screening tools, with the ultimate aim of one day being able to simulate all preclinical toxicology in silico. We hope to help de-risk drug development by driving the design of therapeutic agents with less toxicity, in turn reducing attrition rates across the entire drug development pipeline.

Founding Team

Therence Bois

Co-Founder, CSO

Prudencio Tossou

Co-Founder, CTO

PhD in Molecular Medicine, 

University of Montreal.

Research experience in molecular biology, with a focus on identifying novel targets for drug discovery. Translating scientific evidence into actionable insight for InVivo's technical team.

PhD in Machine Learning, 

University of Laval.

Research experience in bioinformatics and machine learning, with a focus on drug discovery and design. Leading InVivo's research efforts and managing our (rapidly growing) ML team.

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