Binding a desired protein tightly is important for biotechnology. Recent advances in deep learning have allowed the de novo design of (mostly α-helical) binding protein, sidestepping the laborious process of raising antibodies or nanobodies or evolving affibodies, darpins or similar. These deep learning designed binders will bind with okay affinity, but what if the affinity required were much stronger?
<Enter autocatalytic isopeptide bonds>
Another blog post, another look at accepted papers for a major ML conference. NeurIPS joins the other major machine learning conferences (and others) in moving virtual this year, running from 6th – 12th December 2020. In a continuation of past posts (ICML 2020, NeurIPS 2019), I will highlight several of potential interest to the chem-/bio-informatics communities
The list of accepted papers can be found here, with 1,903 papers accepted out of 9,467 submissions (20% acceptance rate).
In addition to the main conference, there are several workshops highly related to the type of research undertaken in OPIG: Machine Learning in Structural Biology and Machine Learning for Molecules.
The usual caveat: given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”). If you find any I have missed, please reach out and I will update accordingly.
Continue readingICML is one of the largest machine learning conferences and, like many other conferences this year, is running virtually from 12th – 18th July.
The list of accepted papers can be found here, with 1,088 papers accepted out of 4,990 submissions (22% acceptance rate). Similar to my post on NeurIPS 2019 papers, I will highlight several of potential interest to the chem-/bio-informatics communities. As before, given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”).
Continue reading