3 articles tagged with #molecular-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce MMAI Gym for Science, a training framework for molecular foundation models in drug discovery. Their Liquid Foundation Model (LFM) outperforms larger general-purpose models on drug discovery tasks while being more efficient and specialized for molecular applications.
Researchers introduce DeMol, a new dual-graph framework for molecular property prediction that explicitly models both atoms and chemical bonds to achieve superior accuracy. The approach addresses limitations of conventional atom-centric models by incorporating bond-level phenomena like resonance and stereoselectivity, establishing new state-of-the-art results across multiple benchmarks.
Researchers have developed EDT-Former, an Entropy-guided Dynamic Token Transformer that improves how Large Language Models understand molecular graphs. The system achieves state-of-the-art results on molecular understanding benchmarks while being computationally efficient by avoiding costly LLM backbone fine-tuning.
