arXiv:2506.10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair, generating structurally valid molecular alternatives with reduced toxicity, has not yet been systematically defined or benchmarked. To fill this gap, we introduce ToxiMol, the first benchmark task for general-purpose Multimodal Large Language Models (MLLMs) focused on molecular toxicity repair. We construct a standardized dataset covering 11 primary tasks and 660 represe
Advances in MLLMs are enabling their application to complex scientific challenges, including those requiring nuanced molecular understanding.
Improving molecular detoxification directly impacts drug development success rates, potentially accelerating the creation of safer therapeutics and reducing R&D costs.
The systematic definition and benchmarking of molecular toxicity repair for MLLMs establishes a new approach to addressing a critical bottleneck in drug discovery.
- · Pharmaceutical industry
- · AI/ML drug discovery platforms
- · Biotech startups
- · Patients
- · Traditional drug discovery methods
- · Companies with high rates of drug candidate failure due to toxicity
Reduced toxicity in early-stage drug candidates leads to more efficient drug development pipelines.
Faster and safer drug development results in a wider array of available treatments for various diseases.
The integration of MLLMs across biological engineering could ignite rapid advancements in synthetic biology, impacting therapeutics and beyond.
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Read at arXiv cs.AI