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MIST foundation models accelerate chemical discovery with novel tokenizer

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MIST, a new family of molecular foundation models designed to accelerate discovery and exploration within chemical space. These models, trained on extensive unlabeled datasets using a novel tokenizer called Smirk, demonstrate strong performance across over 400 structure-property relationships. MIST has shown success in predicting scent profiles, a task not explicitly part of its training, and has been applied to real-world problems in areas like electrolyte screening and organometallic stereochemistry. AI

IMPACT Accelerates materials discovery and design by providing scalable navigation of chemical space.

RANK_REASON Academic paper detailing a new family of foundation models for chemical discovery.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MIST foundation models accelerate chemical discovery with novel tokenizer

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Academic paper detailing a new family of foundation models for chemical discovery.
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136 days old
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alexius Wadell, Anoushka Bhutani, Victor Azumah, Austin R. Ellis-Mohr, Andrew J. Stier, Kareem Hegazy, Alexander Brace, Hancheng Zhao, Celia Kelly, Anuj K. Nayak, Yuhan Chen, Dimitrios Simatos, Hongyi Lin, Murali Emani, Venkatram Vishwanath, Kevin Gering, ·

    Foundation Models for Discovery and Exploration in Chemical Space

    arXiv:2510.18900v2 Announce Type: replace-cross Abstract: Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to navigate…

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