Anthony Gitter
@anthonygitter
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Computational biologist; Associate Prof. at University of Wisconsin-Madison; Jeanne M. Rowe Chair at Morgridge Institute
Joined April 2015
RT @jmarshlab: Out today in PLOS Comp Biol:.Intrinsically disordered regions make variant prediction deceptively ea….
journals.plos.org
Author summary Some parts of proteins, known as intrinsically disordered regions, do not fold into fixed shapes. Instead, they stay flexible and play key roles in controlling how cells work, often by...
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RT @BiologyAIDaily: Variant Effect Prediction with Reliability Estimation Across Priority Viruses. 1. A new framework called EVEREST has be….
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RT @srikosuri: The OpenADMET teams are cranking away. @jchodera @fraser_lab and I started to formalize the organization and we will have a….
openadmet.org
OpenADMET is an open effort to build predictive models of ADMET properties Published: 6 Aug, 2025; 2 minute read Small molecule therapies have always been and continue to be the dominant way we treat...
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RT @duguyuan: 🧬Want to do a user study (Evolla vs. biologists using traditional bio tools like BLAST, Foldseek) on some proteins with kno….
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Do any retrosynthesis prediction algorithms work well enough to be useful in practice? I have a compound that was commercially available and no longer is. It isn't similar to anything in 4 synthesize-on-demand libraries. FutureHouse's Phoenix can't give a retrosynthetic pathway.
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I participated in the ADMET property subchallenge using the Tabular Prior-data Fitted Network (TabPFN) with minimal tuning. It didn't work well and was in the bottom half of the submissions. That's okay!. See my GitHub repo for code and a short writeup:
github.com
The ADMET Challenge of the ASAP Discovery competition - agitter/asap-polaris-admet-challenge
A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data. 1. A recent computational blind challenge focused on drug discovery for pan-coronaviruses has yielded significant insights into the effectiveness of various computational methods. The challenge
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RT @ginaelnesr: The MLSB workshop will be in San Diego, CA (co-located with NeurIPS) this year for its 6th edition in December 🧬🔬. Stay tun….
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Inspired by
did u know you can use the new Gemini image segmentation feature in… a lot of different ways
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It's disappointing the @workshopmlsb was not accepted at NeurIPS this year. Now I have nowhere to submit my new protein structure-based fitness prediction strategy.
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RT @andrewwhite01: HLE has recently become the benchmark to beat for frontier agents. We @FutureHouseSF took a closer look at the chem and….
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RT @brittanytrang: NEW from me @statnews:. We've learned that DeepMind is going to fund CASP, the protein structure competition that brough….
statnews.com
Protein structure prediction contest CASP gets temporary funding from Google DeepMind as NIH grant runs out.
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RT @KexinHuang5: 🤝Excited to partner with Tamarind @kavi_deniz to build towards an agentic AI protein designer. 🔁 Agentic protein optimiza….
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There are many more results and controls in the paper. Here's how the best (most negative) docking scores change when we use relevant assays, irrelevant assays, or no assays as context for generation with GPT-4o. In the majority of cases, but not all, relevant context helps. 6/
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This generally has the desired effects across multiple LLMs and queried protein targets, with the caveat that our core results are based on AutoDock Vina scores. Assessing generated molecules with docking is admittedly frustrating. 5/.
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We embed the BioAssay data into a vectorbase, retrieve initial candidate assays, and do further LLM-based filtering and summarization. We select some active and inactive molecules from the BioAssay data table. This is all used for in-context learning and molecule generation. 4/.
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A proof of concept study from our collaborators showed that mining this PubChem data successfully identified new candidates for a target phenotype, oxidative phosphorylation We wanted to generalize that for any new query and assess the effectiveness. 3/.
jcheminf.biomedcentral.com
Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior...
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PubChem BioAssays can contain a lot of information about why and how an assay was run. Here's an example from our collaborators. There are now 1.7M PubChem BioAssays ranging in scale from a few tested molecules to high-throughput screens. 2/
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Our preprint Assay2Mol introduces uses PubChem chemical screening data as context when generating molecules with large language models. It uses assay descriptions and protocols to find relevant assays and that text plus active/inactive molecules as context for generation. 1/
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