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(iv) targeting the vulnerabilities of pathogens, which often have background mutations that reduce their viability. (v) It is also expected that AI will tackle difficult drug targets. Some of this thinking is based on pre- liminary data with well-characterised targets such as HER211 and RAS12.


Limitations in AI methodologies The term ‘AI’ is used as an all-encompassing umbrella that covers everything from machine learning all the way to network architectures such as Deep Learning (DL). All AI methodologies use combinations of tools such as search, mathemati- cal optimisation, neural networks, probability and economics. The techniques used include Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN), giving rise to DL. Developing machines approaching human level intelligence is among the long-term goals of AI, termed as


Artificial General Intelligence (AGI). The promise of DL, however, is that DL is more than just a col- lection of multiple layers of ANN and has the capacity to evolve into emergent intelligence which is a property displayed by human intelligence. Clearly, AI is viewed as a transformative tech-


nology that can serve as a catalyst for a new approach to drug development – but it is by no means a ‘silver bullet’ for drug discovery and devel- opment. The proverbial Achilles heel of AI is the amount and the quality of data needed for con- structing AI training sets. All AI approaches, including AGI and DL require lots of data (big data) and are not immune to the basic computing rule – ‘garbage in, garbage out’ – which means that neural networks trained on flawed data can be highly error-prone. AI is reliant on three things: high quality data,


high quantity data and data that are relevant to the research question being asked. These data must already be known. AI is great at distilling value


New Easi-CRISPR Technology


The latest evolution in the CRISPR revolution.


Taconic Biosciences’ new Easi-CRISPR capabilities let you perform rapid whole genomic insertions, rather than limiting your projects to point mutations and constitutive knockouts.


With Easi-CRISPR, our genetic engineers deliver more complex models, faster.


 Simplify large genetic insertions, such as conditional knockouts.  Produce viable models up to six months faster than traditional large insertion methods.


 Easily generate a genetically humanized mouse model expressing the human instead of the mouse protein.


Read Taconic’s White Paper, Application of CRISPR/Cas to the Generation of Genetically Engineered Mice at taconic.com/crispr-applications


US: 1-888-822-6642 | EU: +45 70 23 04 05 | info@taconic.com


Drug Discovery World Summer 2018


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