Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug–target interaction predictor can generate target-aware molecules without task-specific retraining. Protein–ligand–pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary.
Karabowicz et al. (Fri,) studied this question.