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Dropping another use case of Faraday changing how scientists work and turning days of work into minutes Query: analyze obesity drugs' effects on muscles old way: manual lit search → collect trial data → compile findings → analyze with LLMs: prompt → get some guidance and
The way scientists work is changing A use case example on Faraday⬇️ Query: Based on Orforglipron targeting GLP-1, generate some structurally distinct molecules that could hit the same target site but give us fresh SAR space to explore Instead of: scientist → days of manual
OK, so the $ bio ai assistants are: Ascent Bio - ascentbio.ai Bio Protocol BIOS - ai.bio.xyz Edison Scientific - edisonscientific.com K-Dense - k-dense.ai Potato - potato.ai SciSpace - scispace.com/biomedical/ Did I miss any?
Today we opened up access to the @AscentBio platform, letting scientists experience what agentic science can be. As a former PhD student, it is exciting to envision an era where researcher productivity can 10x. While not perfect, anyone who’s used Claude code or cursor can see
Today, we’re excited to announce that Faraday by @AscentBio has achieved SOTA performance on the Ether0-MCQ benchmark. This represents a major improvement over existing LLMs and specialized agents in reasoning over molecules. Ether0-MCQ is a 150 question benchmark subset by
The agent that designs molecules on demand—just give it a structure + goal 🧪 (like "improve oral bioavailability but keep the core") Prompt → candidates in minutes. It will: → Analyze parent properties → Preserve key pharmacophore → Identify bioavailability-limiting
Visualize data with ease before: setup env, write visualization code, execute code now: write "plot these molecules"
Launch an agent to design novel derivates of a molecule of interest and watch how it: → gets your reference molecule’s structure and analyzes the pharmacophore and binding requirements for the target → designs structurally distinct scaffolds using scaffold hopping and
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