In a recent preprint from the Baker Lab, Jue Wang et. al. outlined a framework for protein design that uses protein structure prediction neural networks. This framework defines
How do you design a protein that binds to another protein given only the target protein structure? Until recently, you could use Rosetta to manually
What would you give to be able to train a neural network with 5% of the labels that you thought you needed? In the context
In CASP14, DeepMind presented the results of AlphaFold, a deep neural network designed for protein structure prediction. During the experiment, AlphaFold predicted structures with an
This week we discuss a new paper from the creators and authors of EquiDock [see our related blog on EquiDock that explores “Independent SE[3] Equivariant Models for End-to-End
The year 2021 was a Pandora’s Box for machine learning in chemistry. DeepMind put the chemistry world on notice when it published its approach to the protein
Today we break down a paper recently accepted for publication at ICLR 2022: “Independent SE(3) Equivariant Models for End-to-End Rigid Docking” [1]. Docking is the
Recent advances in computational chemistry have modeled non-covalent chemical interactions like protein folding with increasing accuracy [1]. But for chemical reactions that involve bond-breaking and
The European Lab for Learning and Intelligent Systems [ELLIS] recently held a workshop on “Machine Learning for Molecular Discovery” in conjunction with NeurIPS 2021. Highlights
The Internet of Things (IoT) is connecting more and more devices. Read this post to understand how ZONTAL helps effectively manage the flood of data produced. Pharmaceutical companies have
