Multimodal Deep Learning for Protein Engineering | Kevin K. Yang Molecular Modelling and Drug Discovery
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- Science
[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: https://datamol.io/
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Abstract: Engineered proteins play increasingly essential roles in industries and applications spanning pharmaceuticals, agriculture, specialty chemicals, and fuel. Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein property prediction. However, protein datasets contain information in addition to sequence that can improve model performance. This talk will cover models that use sequences, structures, and biophysical features to predict protein function or to generate functional proteins.
Speaker: Kevin K. Yang
Twitter - Prudencio
Twitter - Jonny
Twitter - datamol.io
[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: https://datamol.io/
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also, consider joining the M2D2 Slack.
Abstract: Engineered proteins play increasingly essential roles in industries and applications spanning pharmaceuticals, agriculture, specialty chemicals, and fuel. Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein property prediction. However, protein datasets contain information in addition to sequence that can improve model performance. This talk will cover models that use sequences, structures, and biophysical features to predict protein function or to generate functional proteins.
Speaker: Kevin K. Yang
Twitter - Prudencio
Twitter - Jonny
Twitter - datamol.io
1 hr 2 min