Large language models accelerated health informatics screening framework predicts infrared triggered drug delivery performance of doped hydroxyapatite.

Metal-doped hydroxyapatite (HA) is an excellent material for infrared (IR)-induced on-demand drug delivery applications. However, conventional methods for predicting the optoelectronic and photothermal properties of metal-doped hydroxyapatite require intensive computational efforts using density functional theory (DFT). In this work, we show that fine-tuned large language models, namely Darwin 1.1.5 (a question- answering and multitask fine-tuned LLaMA-7 B model with 28,000 scientific QA pairs and 21 FAIR datasets, including NagasawaOPV and QM 9 datasets with SMILES encoding) and an optical property predictor based on T5, can achieve state-of-the-art accuracy with MAD and RMSE of 0.72 eV and 0.69 eV for bandgap prediction, respectively, compared to PBE- DFT (MAD= 1.65 eV) and GNN predictors (RMSE ≈ 0.85 eV), while reducing the time cost by an estimated 50-70%. The proposed system operates as an evidence-based characterization tool and decision support system, proven by predicting a monotonically decreasing trend in bandgap as the doping concentration increases: the Ag-HA series goes from 4.312 eV (0.25 mol %) to 3.983 eV (0.75 mol %); the Au-HA series from 4.300 to 3.950 eV; the Ti- HA series from 4.000 to 3.600 eV; the Zn-HA series from 4.680 to 4.400 eV; and the Mg-HA series from 4.600 to 4.500 eV, all consistent with hybrid-functional DFT benchmarks. T5 simulations show quadratically increasing absorption between 808 nm (0.5-1.3 a.u. for 1-2 mol % Ag and Au) and plasmonic SPR peaks at 420-432 nm. The predicted photothermal conversion efficiency is 34.7 ± 2% for Ag-HA, while at 445 nm it is 18.8%. On the other hand, NIR-induced tetracycline release from Ag-HA reaches 39.21 ± 2.2.5% at 10 min, which is 2.15 times that of passive diffusion (Computational estimate yet to be verified experimentally). All dopants exhibit substitutional doping at Ca