Rohan Gorantla (he/him)

59 posts

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Rohan Gorantla (he/him)

Rohan Gorantla (he/him)

@gorantlarohan

AI x Drug Discovery @Novartis | prev: PhD @EdinburghUni @BioMedAI_CDT

Basel, Switzerland Katılım Mayıs 2013
361 Takip Edilen339 Takipçiler
Rohan Gorantla (he/him) retweetledi
Leandro von Werra
Leandro von Werra@lvwerra·
We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life. huggingface.co/spaces/Hugging…
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caithmac
caithmac@caithmac·
We just published our work on an explainable active learning framework for ligand–protein binding affinity prediction in Digital Discovery. 🔗 pubs.rsc.org/en/content/art… Here’s a quick breakdown of what we did and why 👇
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Michael Bronstein
Michael Bronstein@mmbronstein·
Apply for the AITHYRA-CeMM International PhD Program! 15-20 fully funded PhD fellowships available in Vienna in AI/ML and Life Sciences Deadline for applications: 10 September 2025 apply.cemm.at
Michael Bronstein tweet media
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Aryo Pradipta Gema
Aryo Pradipta Gema@aryopg·
New Anthropic Research: “Inverse Scaling in Test-Time Compute” We found cases where longer reasoning leads to lower accuracy. Our findings suggest that naïve scaling of test-time compute may inadvertently reinforce problematic reasoning patterns. 🧵
Aryo Pradipta Gema tweet media
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Christopher W. Wood
Christopher W. Wood@ChrisWellsWood·
We're delighted to announce that our conference "Protein Evolution, Design and Informatics Edinburgh 2026" will be running from the 13th-15th of May. Register interest here and please retweet! biochemistry.org/events-and-tra…
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Rohan Gorantla (he/him)
Rohan Gorantla (he/him)@gorantlarohan·
💡Why BALM? While structure-based methods have limitations, they are widely used compared to recent deep learning (DL) models due to generalisability issues. To make DL models reliable for screening, we address challenges at model, data and evaluation levels. 🧵2/9
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Sanjeeva Reddy Dodlapati
Sanjeeva Reddy Dodlapati@dodlapati_reddy·
Unlocking the role of uncertainty in genetic variant predictions: Dive into how understanding and quantifying uncertainty can enhance the reliability of genomic models for personalized medicine @sdodl001/unraveling-uncertainty-in-genetic-variant-predictions-with-deep-learning-a418f89d4378" target="_blank" rel="nofollow noopener">medium.com/@sdodl001/unra…
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Ayush Noori
Ayush Noori@ayushnoori·
Thrilled to share "Empowering biomedical discovery with AI agents" in @CellCellPress, from our team (led by @GaoShanghua) in @marinkazitnik's lab. We envision agentic "AI scientists" that coordinate: - LLMs 🤖 - ML tools 🛠️ - experimental platforms 🧪 - human input 🥼 1/9
Marinka Zitnik@marinkazitnik

Excited to share our perspective in @CellCellPress, where we discuss “AI scientists” as collaborative AI agents designed to empower biomedical research cell.com/cell/fulltext/… While the concept of an “AI scientist” is aspirational, advances in agent-based AI are paving the way for AI agents as conversable systems with reflective and reasoning abilities that coordinate LLMs, ML tools, experimental platforms, or combinations thereof. We outline initial autonomy levels for agents based on proficiency in hypothesis generation, experimental design, execution, and reasoning: Level 0: No AI agents Level 1: AI agents as research assistants Level 2: AI agents as collaborators Level 3: AI agents as scientists These “AI scientists” could enhance discovery workflows by introducing skepticism and reasoning. Our vision is to amplify human creativity, enabling AI to handle large datasets, navigate complex hypotheses, and perform repetitive tasks faster. Many ethical considerations arise with biomedical AI agents. We discuss issues of governance, robustness, evaluation protocols, dataset generation, and associated risks. Imagine AI agents that help: 🔬 Design discovery workflows 🌱 Simulate virtual cells 🎛️ Control phenotypes programmatically ⚙️ Design cellular circuits 💊 Optimize therapeutic discovery and development Many thanks to fantastic group of co-authors @GaoShanghua, @AdaFang_ @YepHuang, @valegiunca, @ayushnoori, @schwarzjn_, @YEktefaie, @kondic_jovana @HarvardDBMI @harvardmed @KempnerInst @harvard_data @broadinstitute

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pkms 🍕
pkms 🍕@PrakamyaMishra·
We've just released our 1B language model! We've provided comprehensive details to make it easy to reproduce. Hope it will be useful for ML/AI developers, especially those working on distributed training with AMD Instinct GPUs. amd.com/en/developer/r… @AMD
Emad Barsoum@EmadBarsoumPi

AMD first 1B LLM model is released!!! proud of the team. We released everything training script, dataset detail, weights, score card and benchmark results. #AMD #LLM #ML #AI #HW #MI300X amd.com/en/developer/r…

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