Charlie Harris

@harrisbio.bsky.social

PhD @ Cambridge in AI for Bio | Interested in generative modelling for drug discovery and science policy 🇬🇧 Website: cch1999.github.io Blog: harrisbio.substack.com Database: harrisbio.notion.site

Small personal update: very pleased to be in Singapore next week to present 2 spotlight papers at ICLR 2025 on AI for molecular design!! 🇸🇬 DM me if you want to meet up and chat about AI for bio, drug discovery, science policy or just chat about aviation!!!

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1/ Just read through the Matt Clifford AI Action Plan now. Tl;dr: it's great but here are a few things that stood out to me as someone interested in AI for Science and sovereign compute and data capability. A thread: 🧵

A common issue I see in ML, both from ML "experts" and "users", is overly optimistic assumptions. "experts" (people designing algs) usually assume the data is very simple "users" (people using algs) usually assume that algorithms are more robust than they really are Conclusion: always be careful!

Just added Graph Therapeutics, a new startup in Vienna focusing on precision medicine for inflammation and immunology Founded by former Allcyte team

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Charlie Harris@harrisbio.bsky.social · 2y ago

🚨I have updated the TechBio Company Database with a bunch of new companies! Find it here: tinyurl.com/techbiodatabase New ones are: - Enveda Biosciences (Natural Products) - Cure_51 (Personalised Medicine) - Antiverse (Antibodies) - Tamarind Bio (SaaS) - Biorelate (Target ID) cont.

Extremely pleased to announce that after *checks notes* 2 years, our paper on Structure-based Drug Design with diffusion models has been published in Nature Computational Science (@natcomputsci.bsky.social)!! Thanks a lot to the great co-authors! Esp @rne.bsky.social & Yuanqi Du.

Structure-based drug design with equivariant diffusion models - Nature Computational Science

This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems

nature.com

In other words, we are still really bad at structure prediction UNLESS we have very rich sequence information from which to infer spatial contacts (?) Same applies to ligand binding obviously

Torsten Schwede@torstenschwede.bsky.social · 2y ago

Conclusion of RNA assessors in #CASP16 - we are still in the “template” phase. Prediction of nucleic acids structure is still challenging for targets without templates, often relying on intuition. No big leap in accuracy since CASP15. AI based methods have not (yet?) made a major difference.

Conclusion slide on RNA assessment

Was very fortunate to be invited to give a talk on AI for Drug Discovery at the (very nice) British Ambassador's Residence in Rome last week! Thanks to the Foreign Office for the invitation!

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Actually a bit sad that I have never, and probably will never, attend CASP:( (unless I get a lot of spare time and funding) So important for driving the revolution in AI for structural biology that’s still taking place right now!

Random thought: Did the DL x proteins academic research community sort of move on from antibody design to enzyme design? Everyone following the trend? Some of the discourse around antibodies may make it seem like de novo design given any target is ‘solved’, but this is not true as far as I know…