Censoring chemical data to mitigate dual use risk

Machine learning models have dual use potential, potentially serving both beneficial and malicious purposes. The development of open-source models in chemistry has specifically surfaced dual use concerns around toxicological data and chemical warfare agents. We discuss a chain risk framework identifying three misuse pathways and corresponding mitigation strategies: inference-level, model-level, and data-level. At the data level, we introduce a noising method to increase prediction error in specific desired regions (sensitive regions). Our results show that selective noise induces variance and attenuation bias, whereas simply omitting sensitive data fails to prevent extrapolation. These findings hold for both molecular feature multilayer perceptrons and graph neural networks. Thus, noising molecular structures represents a step toward enabling safer sharing of potential dual use molecular data.