For the last decade, the field of deep learning and AI has been dominated by applications to images and text. However, in the past two years, the field has seen an upsurge of chemical and biological applications. The international conference on learning representations [ICLR], is the largest academic AI conference in the world, with an h5-index […]
Extremely data-efficient ligand generation What is a sufficient number of data points to train a deep learning algorithm? 1,000? 1 million? 1 billion? Of course, it depends on the problem. But it also depends on the neural network architecture and training algorithm chosen to solve the problem. Powers et. al. recently published a preprint describing a ligand […]
The study of structural biochemistry is based on the axiom that “structure determines function”. A corollary of that axiom is that, for proteins, “function is independent of sequence, given structure”. That is, knowing the sequence of a protein is helpful for determining function insofar as it informs us about the structure of the protein. Many […]
In structure-based drug discovery, most methods rely on two key elements of accuracy: accurate protein structure modeling and accurate drug structure modeling. AlphaFold is able to predict protein structures with unprecedented accuracy. But drug structure modeling lags behind, with current models for conformer generation only providing 67% accuracy on a common molecular conformer benchmark. GeoDiff predicts drug conformations with […]
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 several types of protein design problems and demonstrates multiple effective approaches to solve these problems. Here we condense this detailed study into its main points. Protein Design Tasks: The […]
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 craft a protein using expert heuristics. However, this process is laborious, expensive, and does not generalize. Researchers at the Institute for Protein Design recently published a groundbreaking work outlining a systematic process […]
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 of drug discovery, that could be a cost reduction of 95% for expensive experiments. Today we discuss a new paper from Meta AI, which provides a general algorithm for self-supervised learning. […]
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 average deviation of ~1 Å from the C-alpha atoms of experimentally solved structures. Now, ~1 Å is often also used as the resolution to denote high accuracy experimental structures (they […]
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 folding problem [1]. I expect that we will continue to see machine learning approaches quickly dominate the well-defined, data-rich problems in chemistry. However, there are other challenges that are harder to […]
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 problem of finding the pose and orientation by which a ligand binds to a protein. Solving the computational docking problem would increase our understanding of biological interactions at the molecular […]
