Artificial Intelligence For Drug Discovery Use Cases At Mind the Byte

by David Vidal    Contributor        Biopharma insight / White Papers And Industry Reports

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Topics: Emerging Technologies   

UPDATE: Mind the Byte ceased operations and was liquidated in 2019.

Artificial intelligence (AI) has become a hot topic in the biopharmaceutical environment and nearly every pharma company in the world has embraced it hoping that it will play a major role in speeding up drug discovery, by reducing R&D costs and avoiding failure in late development stages. According to prospects, AI-driven drug discovery will lead to the development of new and more effective drugs, paving thus the way to personalized medicine.

Machine learning (ML) techniques, a particular approach to artificial intelligence, are currently being used at Mind The Byte to develop new In Silico tools for Drug Discovery as well as for the improvement of classical CADD techniques. Different supervised learning algorithms; artificial Neural Networks (aNN), Support Vector Machines (SVM) and Random Forest (RF), are being applied in four different research areas: ADMET modeling, In Silico MedChem, QSAR and Docking:

(To know more about other AI use cases in drug discovery read our recent review "How Pharmaceutical And Biotech Companies Go About Applying Artificial Intelligence in R&D")


ADMET modeling: In order to support selection of druggable chemotypes among screening hits and potential target molecules, we have developed a set of relevant predictive in silico models. Using in vitro data extracted from different databases and publications, conveniently mined, curated and standardized, different ADME predictive models have been generated using the different ML modeling techniques previously summarized. Developed ADME models include physicochemical (logP, logS) and pharmacokinetic (Caco-2, BBB, %PPB, Pgp and hERG) properties.

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Topics: Emerging Technologies   

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