Matt McPartlon
@Mattmcpartlon1
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RT @kenbwork: Some scientists see the increasingly impressive results from protein binding models and argue "solving" this problem actually….
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RT @BiologyAIDaily: Chai-2: Zero-shot antibody design in a 24-well plate. １．Chai-2 is a multimodal generative model for protein design that….
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RT @AriWagen: Chai Discovery (@joshim5 @chaidiscovery) just released Chai-2, a model for "zero-shot antibody design in a 24-well plate.". T….
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It's been a privilege work alongside the most uniquely talented group of individuals I've ever encountered on this breakthrough result. @Kevin_E_Wu, @_nathanrollins, @j_boitreaud, @danny_nkjg, @_jackdent , @joshim5, @ZhuoranQ and others. You all made this possible!.
We’re excited to introduce Chai-2, a major breakthrough in molecular design. Chai-2 enables zero-shot antibody discovery in a 24-well plate, exceeding previous SOTA by >100x. Thread👇
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RT @chaidiscovery: We're excited to partner with the UK Government on OpenBind, an effort to create the largest open dataset of drug-protei….
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RT @chaidiscovery: Chai-1 has always been available for commercial use via our server. Today, we're also making Chai-1(r) code and weights….
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RT @chaidiscovery: Today we’re releasing Chai-1r, an updated version of Chai-1 with full support for restraints. Prompting Chai-1r with pri….
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RT @MartinPacesa: Have you ever wanted to design protein binders with ease? Today we present 𝑩𝒊𝒏𝒅𝑪𝒓𝒂𝒇𝒕, a user-friendly and open-source pip….
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RT @sokrypton: Exciting new work from Qian Cong's group on predicting human protein interactome. Leveraging new eukaryotic genomes, new Ros….
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RT @Stanford_AI_Bio: Next Tuesday, we are super excited to have @Mattmcpartlon1 from @chaidiscovery to share Chai-1, a foundation model for….
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RT @OleinikovasV: Having previously only joined remotely and following numerous @RDKit_org UGMs from afar, I was super excited to join this….
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RT @joshim5: We’re excited to introduce @ChaiDiscovery and release Chai-1, a foundation model for molecular structure prediction that perfo….
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Our Latest work on Flexible Docking . Deep Learning for Flexible and Site-Specific Protein Docking and Design . @jinboxu_chicago.
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RT @biorxiv_bioinfo: Deep Learning for Flexible and Site-Specific Protein Docking and Design #biorxiv_bioinfo.
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RT @biorxivpreprint: Deep Learning for Flexible and Site-Specific Protein Docking and Design #bioRxiv.
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RT @jinboxu_chicago: An end-to-end deep learning method for rotamer-free protein side-chain packing
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RT @jinboxu_chicago: A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding .Our latest work on fixed-….
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RT @KevinKaichuang: Represent proteins as sequence + distances + orientations + torsion angles and use a VAE to generate novel backbones.….
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