MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

2026-03-03

AI Summary

This work addresses the limited scientific reasoning capabilities of general-purpose large language models (LLMs) in drug discovery, which cannot be reliably improved merely by scaling up model size or incorporating generic reasoning mechanisms. To overcome this, the authors introduce MMAI Gym for Science—a platform that integrates multimodal molecular data with task-specific training, inference, and evaluation frameworks—and propose a lightweight Liquid Foundation Model architecture designed to effectively model “molecular language.” This approach achieves state-of-the-art or competitive performance across key tasks including molecular optimization, ADMET prediction, retrosynthesis, and drug–target activity prediction, while requiring substantially lower computational resources than both larger general-purpose and specialized models, thereby breaking the performance bottleneck of general LLMs in specialized scientific domains.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain.

Problem

• drug discovery
• foundation models
• molecular understanding
• large language models
• scientific reasoning

Innovation

• Liquid Foundation Model
• MMAI Gym
• molecular reasoning
• drug discovery
• foundation model training