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• General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks.limitation 90%
• Simply increasing model size or introducing reasoning tokens does not yield significant performance gains for drug discovery tasks.limitation 85%
• The MMAI Gym for Science is a one-stop shop for molecular data formats, modalities, and task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules'.method 95%
• Smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks.result 90%
• The Liquid Foundation Model (LFM) trained with MMAI Gym achieves near specialist-level performance across essential drug discovery tasks including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning.result 85%
• The Liquid Foundation Model (LFM) surpasses larger models in the majority of settings while remaining more efficient and broadly applicable in the domain.result 85%
• The MMAI Gym for Science is designed to solve practical drug discovery problems.technical 90%

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