Yep... https://t.co/MmYgSyEvT9

Predicting TCR antigen specificity at proteome‑scale with synthetic immune cells and machine learning. I’m pleased to share our group's collaborative effort with Engimmune in this new pre‑print:

Sharing our computational immuno ecosystem - started for internal use but might help others too! CRAN package >100 functions (both custom&external tools eg cellphoneDB,projecTILs,alphafold) Database to download&integrate scSeq/VDJ directly into R + ~20 tutorials w/ code,data,text

Online now: High-throughput T cell receptor engineering by functional screening identifies candidates with enhanced potency and specificity

Update: Our deep mutational learning #DML paper on #SARSCoV2 profiling is now online at @CellCellPress! Now with more in-depth assessments of a greater number of antibodies! https://t.co/SIh8ltLl2E

Proud to share our *finally* accepted paper. It was a wild ride, but we finally got there on my last day at @ETH_BSSE @ETH. Couldn't ask for a better way to finish my time in the @ReddyLab_ETHZ. https://t.co/eAz9AHZLq4

Protein engineers work wonders with machine learning, but generating data remains a bottleneck. Computer vision & NLP use data augmentation, but we lack such techniques for proteins. To address this, we develop nucleotide augmentation (NTA). A thread 🧵1/5

Landing therapeutic genes safely in the human genome https://t.co/8QOkbDeKnF via @wyssinstitute

Highlighting our recent work from @ReddyLab_ETHZ involving the construction of combinatorial RBD libraries that were used to infer and validate ACE2 binding and escape from clinically relevant mAbs. Moving towards in silico prediction of escape for emerging variants🤓

The lesson: individual mutations cannot predict the behavior of combinatorial mutations. Read more: https://t.co/S8DQPLLgib https://t.co/9ePpxdRGwo @ReddyLab_ETHZ

Spinoff deepCDR Biologics acquired by Boston-based company Alloy Therapeutics @AlloyTx The acquisition of #deepCDR adds expertise in #Bioinformatics and #MachineLearning to Alloy’s #antibody discovery workflow. > https://t.co/WuIdj0cEIy #ETHspinoff @ETH_en @ReddyLab_ETHZ

Another Omicron neutralization thread, this time about monoclonal antibodies. Once again, massive cred to @DannySheward, and everyone else from the previous data release. This time, we also need to especially thank @ReddyLab_ETHZ (1/n)

As reported in the Economist this summer, it may indeed be possible to predict viral evolution by combining #AI and #ML methods. Check out our latest preprint: https://t.co/eKOh4ANzA1

Predictive profiling of SARS-CoV-2 variants by deep mutational learning https://t.co/NNAeZFDfrT #bioRxiv

It has been 2 years since the 1st detection of #SARSCoV2 and yet the #pandemic shows little signs of slowing down, as seen most recently through the emergence of #omicron and its surprisingly large No. of mutations. How surprising is that though? A thread.

We are thrilled to join Alloy on its mission of empowering scientific entrepreneurs and democratizing foundational drug discovery capabilities

Congrats to Joseph Taft for being a poster winner at #PEGSEurope

Catch Prof. @saireddy911's and Dr. @JosephMTaft's presentations at #PEGS Europe 2021 in Barcelona. When: today Topic: Predictive profiling of #SARS evolution using #DeepLearning Find us during the poster sessions this afternoon (posters 11,12,13,14). See you there!

DeepSARS: new tool developed with @TanjaStadler_CH, @LabOxenius, F. Rudolf, @AlexYermanos and several other great collaborators, we hope to improve #COVID19 diagnostics & early detection of #variants by genomic surveillance of #coronavirus #SARSCoV2 https://t.co/U5nvf2QDwt

🤔 Combating future viruses – Predicting viral evolution may let vaccines be prepared in advance