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Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

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/ Never miss another M2D2 talk, add the schedule to your calendar: https://m2d2.io/talks/m2d2/about/ Also consider joining the M2D2 Slack: https://m2d2group.slack.com/join/shar... 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 -   / kevinkaichuang   Twitter Prudencio:   / tossouprudencio   Twitter Jonny:   / hsu_jonny   Twitter datamol.io:   / datamol_io   ~ Chapters: 00:00 - intro 05:22 - Using Multiple Data Modalities Discover and Design Proteins 08:31 - Signal Peptides Simplify Protein Production 10:42 - Machine Translation for SP Generation 15:24 - Q+A 18:57 - Expanding the Function Space 21:18 - FoldingDiff 38:16 - Discrete Diffusion Mutations 44:57 - OA-ARDM 56:12 - Can we Generate Proteins With New Functions? 58:34 - Q+A