MolDeTox Benchmark Targets Molecular Detoxification via LLMs
Researchers have introduced MolDeTox, a new benchmark aimed at assessing large language models (LLMs) and vision language models (VLMs) specifically for molecular detoxification. Current benchmarks for toxicity repair are hindered by insufficient data diversity, poor structural validity of the molecules produced, and a heavy dependence on proxy models for toxicity evaluation. MolDeTox overcomes these challenges by facilitating precise and trustworthy assessments of toxicity-aware molecular optimization through stepwise tasks. This benchmark capitalizes on the latest advancements in LLMs and VLMs for drug discovery, concentrating on altering molecular structures to diminish toxicity while preserving effectiveness. The findings are documented in arXiv:2605.12181v1.
Key facts
- MolDeTox is a benchmark for molecular detoxification using LLMs and VLMs.
- Existing benchmarks overlook toxicity-related challenges.
- Current toxicity repair benchmarks have limited data diversity.
- Generated molecules often have low structural validity.
- Proxy models for toxicity assessment are heavily relied upon.
- MolDeTox enables fine-grained and reliable evaluation.
- Evaluation is designed for stepwise toxicity-aware molecular optimization.
- The paper is available on arXiv with ID 2605.12181v1.
Entities
Institutions
- arXiv