The machine learning tool box has revolutionized our ability to design novel molecular entities (e.g. proteins) well beyond what the natural repertoire has explored. Despite the incredible advances, the de novo generation of functional molecules in biological concepts remains an incredible challenge.
In this talk I will present some of the efforts in our group in designing both proteins and small molecules. Particularly emphasizing different modalities of molecular representation and the interplay with generative ML to facilitate the exploration of unimaginably large spaces of possibilities. Importantly, many ML-based approaches for molecular design fall short in terms of generalization and sampling off the learned distribution, I will present some ideas on how representation can help to overcome some of these limitations.
Finally, I will present some of the approaches developed in our group to embed function into the designer proteins and how we are suing these components in cellular systems to control the output of these complex biological devices.
Link to join virtually: https://cam-ac-uk.zoom.us/j/81322468305
This talk is being recorded.
