Mikołaj Mizera

AI for Drug Discovery

[Mikołaj Mizera @AjMizera]

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

[Rim @RimShayakhmetov]

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. https://t.co/pf1jb91Ocp

[Insilico Medicine @InSilicoMeds]

🧬 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

[Insilico Medicine @InSilicoMeds]

🚀 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.

[Insilico Medicine @InSilicoMeds]

Think your foundation model is good at drug discovery?🧪

Prove it on clean data and earn your place on the leaderboard.
Every frontier model is already there. None of them wins everything.

Insilico DDD Benchmark. Submissions are open.
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