<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A deep-learning view of chemical space designed to facilitate drug discovery · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2002.02948/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2002.02948/feed.xml" rel="self" type="application/rss+xml" /><item><title>A deep-learning view of chemical space designed to facilitate drug discovery — mentioned by tchitra</title><link>https://news.ycombinator.com/item?id=22819925</link><guid isPermaLink="false">urn:hackerpapers:mention:2002.02948:22819925</guid><pubDate>Thu, 09 Apr 2020 05:04:18 GMT</pubDate><description>&lt;p&gt;That&amp;#x27;s a good question and there are a number of ways to try to tackle this. One of the main reasons you cannot do QM simulations directly is that the high quality methods can cost Omega(n^6/eps) to get eps. relative accuracy (you can do better with DFT, but then you&amp;#x27;re making your life hard in other way). At a high-level (and I mean, 50,000 ft. level), here are the simplest way: 1) Do quantum mechanics simulations of interactions of a small number of atoms — two amino acids, two ethanol molecules. Then fit a classical function to the surface E[energy(radius between molecules, angles)], where…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2002.02948/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
