AI for Drug Discovery
Poland
Joined January 2017
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How do you make a foundation model fluent in drug discovery? 🧬💊
Excited to present our paper at #EMNLP2026:
“MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery”
400+ tasks. One goal: teach Liquid AI models the language of drug discovery.
Come chat!
A specialist with no calibration step outcalibrates a calibrated generalist 🤔
A GB model trained on MMAI Gym data beats Jev on both discrimination and calibration across five CYP inhibition tasks.
Balanced accuracy: 0.74-0.87 vs 0.49-0.67.
@InSilicoMeds
Mikołaj Mizera retweeted
Thought pharma was too complex for Jev-like models? Don't think twice.
A plain-language guide to System 1 in drug discovery: what Jev actually does, why new molecules are the hard part, and how small language models answer, fast and accurately, today.
Mikołaj Mizera retweeted
🧬 Rentosertib went from an AI-designed molecule to patients and recently showed shifts toward younger predicted biological age across 6 proteomic aging clocks.
The first experiment is done. Now you can try our new SOTA+ drug discovery models yourself.
InsilicoMMAI-Chem-ADMET achieves SOTA+ performance on 7 drug safety and metabolism prediction tasks. 🚀
It’s a 4B language model trained on 140K+ datapoints in our MMAI Gym for Science.
#insilicoSOTAFM
Mikołaj Mizera retweeted
🚀 5 new drug-discovery specialist LLMs. SOTA-level performance across 70+ benchmark tasks.
Insilico Medicine has released a series of frontier Specialist Language Models for chemistry and biology, trained through MMAI Gym.
They cover drug safety, potency prediction, chemical synthesis, and biology.
🧬 From general-purpose models to scientific specialists.
