ScienceJune 11, 2020

Generative Therapeutics Design

AI-based Ideation Engine for Biopharma   Bringing a novel therapeutic to patients…
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Ton VAN DAELEN

AI-based Ideation Engine for Biopharma

Bringing a novel therapeutic to patients is difficult, expensive and time-consuming. The average cost of developing a drug and bringing it to market is about $3 billion and can take 12-14 years. The drug discovery phase, which consumes about a third of the overall cost, requires the synthesis of thousands of molecules and up to 5 years to develop a single pre-clinical lead candidate. Furthermore, only 10% of the compounds that enter Phase I trials actually receive approval. We believe that Artificial Intelligence (AI) has the potential to speed up the discovery phase and lower discovery costs significantly. As an additional benefit, AI can help scientists send higher quality compounds to the clinic, reducing the failure rate. Recent advances in molecular science and machine learning, combined with the availability of powerful cloud compute platforms, are turning this potential into reality.

BIOVIA Generative Therapeutics Design (GTD) improves and accelerates lead candidate design by automating the virtual creation, testing and selection of novel small molecules. The cloud-based solution employs advanced AI/Machine Learning techniques to help scientists decide what molecules to make next—helping to guide the drug discovery process and optimize R&D output.

Active Learning

Active learning is a specialization within Machine Learning in which computation (the ‘virtual’) and experiment (the ‘real’) are combined—allowing scientists to find optimal answers in the most efficient way possible. Using small molecule lead discovery as an example, a drug discovery team starts with an initial model built from a small amount of data, e.g., assay results for a few tens of compounds. They then use this model to suggest new compounds that can improve the scope of their models. As they synthesize and assay a series of new compounds, new training data becomes available to retrain and improve the models. Iteratively updating the model in this way is a well-established approach to optimizing designs using the fewest iterations, hence shortening the overall discovery timeline.  As the scope and quality of the models improve, compounds recommended for achieving the desired target product profile (TPP) will become more diverse and more likely to be successful.

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