Year: 2026
Type: preprint
Abstract: We explored the use of post-training quantization (PTQ) to enhance the efficiency of reasoning language models for chemical retrosynthesis in drug discovery. We specifically evaluated the RetroDFM-R model, demonstrating that reducing numerical precision to 4-bit significantly lowered GPU memory requirements while lowering Top-1 accuracy by only about 2 percentage points (3–4% relative) for NormalFloat-4 (nf4) and double-quantized nf4 (nf4dq). The accuracy for 8-bit quantization (int8_ao) was equivalent to the original model but reduced energy consumption by about 17% when only one prediction was required. By testing various quantization methods across different hardware configurations, the study revealed that these techniques allow complex chemical predictions to run on budget-friendly, consumer-grade GPUs. In the augmented setting the 4-bit models returned more distinct candidates, which raised Top-10 accuracy on the full test set by up to 0.8 percentage points; on reactions for which every model filled ten candidate positions, the original model remained the most accurate. In summary this study presents the first evaluation of PTQ applied to RetroDFM-R, a reasoning language model, for chemical retrosynthesis. Ultimately, the research suggests that quantization is a vital tool for making advanced chemoinformatics tools accessible to academic researchers who can run it on cards with smaller GPU memory and can reduce the energy per molecule by about 17 - 30% in the default setting, although it increases it by 30–35% in the augmented setting.
Source: ChemRxiv
Authors: Abdulwahab Hussein, Igor V. Tetko
Institutions: Université de Strasbourg, Helmholtz Munich
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223356793