Varun Ullanat

[Bonnie Berger Lab @lab_berger]

We introduce 🌿 MINT (Multimer Interaction Transformer) – a Protein Language Model (PLM) trained on 96M protein-protein interactions (PPIs) to predict binding affinity, mutational impacts, & antibody interactions better than existing PLMs.

🔗Code: https://t.co/GqrXMjk3Mc
🧵👇

[Marinka Zitnik @marinkazitnik]

Predicting clinical outcomes of drug combinations from preclinical data is a major challenge @YepHuang

We know a drug works in the lab. But will it work in patients? 🔬 ➡️ 🏥

This is key for safe and effective therapies and it's one of the hardest challenges in medicine. MADRIGAL is a multimodal AI model that predicts clinical outcomes of drug combinations from preclinical data 🧵

Why does this matter?
Combo therapies can improve efficacy and reduce side effects, but identifying safe and effective pairs is difficult. The search space is enormous, pharmacological interactions are complex, and many compounds lack complete preclinical data

The missing data problem
Most AI models struggle when key drug data is missing. MADRIGAL learns from incomplete datasets at both training and inference, making it capable of predicting clinical outcomes even for drugs with sparse data

What is MADRIGAL?
A multimodal AI model that integrates 21,842 compounds and predicts 953 clinical outcomes to assess:
✔️ New drug combinations
✔️ Drug safety and toxicity across organs
✔️ Personalized response using patient genomic data

Led by a stellar PhD student @YepHuang with a team of fantastic collaborators @xiaorui_su, Varun Ullanat, Ivy Liang, Lindsay Clegg, Damilola Olabode, Nicholas Ho, Bino John, Megan Gibbs

@HarvardDBMI @Harvard @harvardmed @broadinstitute @KempnerInst @harvard_data

[Mohit Kumar Jolly @mkjolly15]

Happy to share our work on dynamics of coupled cellular decision-making feedback loops in @BiosciencesOf @IAScBng, led by @kishorehari139 & @PradyumnaVH @BSSE_IISc, with contributions from Aditi & @VUllanat (BTech students @RvceUpdates), @TheAviatorFrame.
https://t.co/NEbeXWwN3h

[Shruthi Viswanath @shruthiLab]

PriSM (precision for integrative structural models) is now in press. It is an efficient method to annotate high and low-precision regions in integrative models. @VUllanat and @n_kasukurthi did all the hard work, moving fast and breaking things!
https://t.co/Y6V2YDrjqU

[Jeffrey West @mathoncbro]

This week in #MathOnco 1⃣9⃣4⃣:

<life histories, network models of plasticity, model-informed precision dosing, coordination games, Hawk-Dove control theory, and more>

https://t.co/hfXh7e9PNp

Cover art by: @TheAviatorFrame & @kishorehari139 💯😎

[Mohit Kumar Jolly @mkjolly15]

Excited to share our preprint on how "teams" of epithelial & mesenchymal drivers inhibiting each other drive the landscape of large regulatory networks, thus driving canalisation of cell phenotypes https://t.co/vbMe42O1S1 Plz check out the thread (1/6) @MenonBioPhysics @rikdrprof