Bolek: A Multimodal Language Model for Molecular Reasoning

AI models and platforms for molecular science underpin high-stakes applications in drug discovery, yet the systems delivering them are largely opaque: they expose either a score and a binary answer with no rationale, or fluent prose rarely anchored in molecular structure. We introduce Bolek, a compact multimodal language model that injects a molecular embedding into an instruction-tuned decoder to predict molecular properties and explain them in auditable terms. Bolek improves over its Qwen3 base on every task group of a 30-endpoint TDC panel and outperforms the chemistry-specialist TxGemma-9B-Chat on 11 of 17 ADMET endpoints and on four of the five endpoints held out from its training. Together, these results suggest that targeted modality injection paired with reasoning supervision tied to verifiable features can match domain-specialist systems at a fraction of the parameter count while producing auditable explanations for downstream decisions.