arXiv:2606.19496v1 Announce Type: new Abstract: Generative models can produce individually plausible samples while deviating substantially from a target set in the distribution of key features. For example, a model pretrained on broad drug-like chemical space may generate molecules whose molecular features differ from those of a therapeutic class of interest, such as known antibiotics. Correcting such distributional miscalibration is challenging: direct finetuning on the target set can overfit and does not control which features are matched. To fill this gap, we introduce kernel Calibrating Ge
The proliferation of generative models across various domains, including drug discovery, highlights the increasing need for precise control over generated outputs to ensure practical applicability and safety.
This development allows for more reliable and targeted application of generative AI, particularly in sensitive fields where distributional accuracy of generated features is critical.
The ability to fine-tune generative models to match specific feature distributions without overfitting marks a significant improvement in their utility and control.
- · Drug discovery companies
- · Generative AI developers
- · Precision medicine
- · Materials science
- · Companies relying on less precise generative model outputs
- · Trial-and-error R&D approaches
Generative models can now produce outputs that more closely align with desired real-world characteristics, such as specific molecular features for antibiotics.
This improved calibration could accelerate development cycles in fields like pharmaceutical research by reducing the need for extensive post-generation filtering or refinement.
The enhanced trustworthiness and control of generative AI could lead to its integration into more regulated and critical industries, shifting discovery paradigms.
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Read at arXiv cs.LG