Pipeline & Science OLD

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Pipeline & Science

AI specifically designed to leverage biology and address the challenges of pre-clinical drug discovery and development.

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Pipeline

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Our vision at OneThree is to combine the best biology with the best computation – to find new cures for patients in need.

Thanks to convergence of these fields, it is now possible to think about complex questions surrounding disease biology and drug discovery in a way that previously wouldn’t have been possible. We are now at a place where computational techniques can drive research and experimentation rather than simply being tools for routine data analysis.

Our technology has led us to be able to accurately generate new testable insights and hypotheses by combing the world of systems and disease biology and the best computational tools to discover new treatments for patients in need.

The resulting insights have fueled our internal discovery programs and pipeline targets, seen below.

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Looking Beyond One Part of the Pipeline

It’s just as important to know a drug can treat brain cancer as it is to know whether that drug will cause severe side effects when it binds proteins in the liver, or if the compound can even cross the blood brain barrier to begin with. Over 95% of compounds that enter preclinical development fail before ever making it to patients, and it’s often because issues like these are overlooked in early discovery. Because of this, we built our platform to provide insight across multiple parts of drug development pipeline which not only provides greater mechanistic insights, but also helps ensure a greater success rate for drugs entering preclinical development.

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Decoding Specific Biology

AI/ML isn’t a magic bullet, but if focused on decoding specific biological questions, it has the potential to quickly generate new insights and hypotheses that otherwise might have been missed. That’s the approach we take at OneThree.

Every one of our technologies addresses a specific biological question such as binding to a given target class, gene essentiality in specific genotypes, or target/structure derived toxicity. Our ML scientists and computational biologists work closely with the experimental team to thoroughly evaluate how the hypotheses and insights derived from the platform could impact the development process. This not only allows us to better design and test new systems, but it also makes sure the output of any predictive algorithm can be used to answer direct mechanistic questions and uncover new biology.

Using an example, a traditional AI platform may answer the question “yes or no” when asked if Gene A relevant to breast cancer. However, OneThree’s answer to the same question would be that “the inhibition of Gene A is efficacious and safe in triple negative breast cancer patients with a KRAS mutation, through a synthetic lethal mechanism.”

It’s our way of opening the AI “black box.”

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Data-Driven Discovery

Most existing computational approaches were built and optimized for only a limited number of data types. We’ve spent over a decade working in biology and chemistry labs and know that each type of data is only a piece of a much bigger puzzle, and we brought this mentality into OneThree.

Over time we have built the most diverse, fully integrated database. Our algorithmic models have been tailored to analyze all of the data types relevant to their predictive functionality. To illustrate this point, we have provided two examples below:

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When we predict compound toxicity we integrate 100+ features from 12 different data types, including compound and protein structures, genetic interaction networks, CRISPR and shRNA screens, and tissue-specific genomic profiles. This layered data integration provides us with a better understanding of the underlying cause of any predicted adverse events.
When identifying synthetic lethal (SL) genes, we first mined journal articles and published knockdown screens to build our machine learning models on top of. We then integrated genomic data from cancer patients and cell lines, known metabolic pathways, data from model organisms, and 30+ gene level features to predict new SL pairs. This approach led to a much higher accuracy and greater number of predicted SL pairs that could be exploited therapeutically.

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Our data is derived from over 200 different sources, including: non-profits, major pharmaceutical companies, academic databases, all publicly available data sources, and much more. We have over 50 different types which span biological, chemical, and clinical domains.

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