<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rishabh Anand — HTBAA Fall 2026 :: rishabh-anand</title><link>https://pages.htgaa.org/2026b/rishabh-anand/index.html</link><description>About me Hello!! I’m Rishabh (or Rish), a first-year MS/PhD in the Molecular Machines group at MIT Media Lab building and validating AI methods for molecule design and drug discovery. I think AI has huge potential for making new biomedicine and therapeutics that go beyond human intuition, and HTBAA is exactly where I’d love to see just how much.</description><generator>Hugo</generator><language>en</language><atom:link href="https://pages.htgaa.org/2026b/rishabh-anand/index.xml" rel="self" type="application/rss+xml"/><item><title>HW1: De novo design of Foci Protein Antagonists with Deep Learning</title><link>https://pages.htgaa.org/2026b/rishabh-anand/week-01-hw-project-ideas/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://pages.htgaa.org/2026b/rishabh-anand/week-01-hw-project-ideas/index.html</guid><description>Rishabh Anand (rshb@mit.edu) • HTBAA Homework 1 • Sept 2026&#10;What you would like to biomanufacture and why? DNA randomly undergoes damage, and special dedicated Foci proteins are involved in DNA repair in the nucleus. I’d like to use deep learning methods to de novo design antagonist binders to RAD51, one such enzyme involved in this process (among other such proteins). If we can disrupt the DNA repeat mechanism, we can potentially use such binders to stop cancer cells from fixing their DNA and replicating.</description></item><item><title>Week 3 HW: CFPS Coopetition</title><link>https://pages.htgaa.org/2026b/rishabh-anand/week-03-cfps/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://pages.htgaa.org/2026b/rishabh-anand/week-03-cfps/index.html</guid><description>Copy and paste this section (section 3, A-F) into your website! Note, coopetition.htgaa.org is intended to support you as you answer the following questions.&#10;Goal: using the collective intelligence of the HTBAA community and accessible AI systems, design novel CFPS reaction compositions for each of the three target proteins: sfGFP, PETase, and Reteplase to reduce the cost per gram of functional protein. Each design should be compatible with the experimental framework described above and should reflect the distinct expression and folding requirements of that protein. You can learn more about the details of the coopetition here.</description></item><item><title>Final Project</title><link>https://pages.htgaa.org/2026b/rishabh-anand/final-project/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://pages.htgaa.org/2026b/rishabh-anand/final-project/index.html</guid><description/></item></channel></rss>