How artificial intelligence is reshaping protein structure prediction and therapeutic design – from small molecules to new modalities

Jens Meiler

/Leipzig [DE]

AlphaFold is revolutionizing protein structure prediction. Not because of its increased accuracy in comparison to the best predictions of prior methods, but because of consistent accuracy across all folded, well-structured proteins. I will discuss some of the remaining challenges such as predicting all biologically relevant conformations of flexible membrane proteins including transporters, ion channels, or receptors.

With the availability of highly accurate structural models for most proteins, structure based drug discovery is experiencing a renaissance. The availability of large 'make-on-demand' compound libraries coupled with computational ultra-large library screening fundamentally changes the paradigm in (academic) probe and drug development projects to 'in silico' first! I will introduce these concepts and detail several new algorithms to accomplish these tasks.

While AlphaFold is close to a golden bullet for protein structure prediction, computational design of protein and pepti detherapeuti ccandidates is much more challenging even with the use of artificial intelligence. One challenge is that the desired goal is functi on, the design algorithm focuses on structure implying that it might have the target function. A second challenge is the inclusion of chemical space with limited training data such as non-natural amino acids. I will give an overview of several new algorithms developed by us and others combined with illustrative applications.

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