The Future of Computational Biochemistry
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Computational biochemistry allows drug-makers to cut out a significant portion of the test tube experiments. Instead, a computer simulates the protein and tests all of its atomic interactions. That analysis will yield a far conscise list of leads that researchers can take to the next stage of testing
Benefits for the company
Deep learning truly has revolutionized drug discovery, as it can factor in everything from possible toxicity risks to new applications for existing drugs, which subsequently are saved the expense of a Stage 1 trial.
Feasability
High
Type of expertise/ AI domain
Deep Learning
Internal data required
Different Protein interaction, genomic data, bio- markers etc
Recent experimental techniques (including parallel synthesis of drug-like compounds) has drastically increased the amount of available data for deep learning models. This makes such models adept at bioactivity and synthesis predictions, in addition to molecular design and biological image analysis.