arXiv AI August 24, 2026 By Marvellous O. Ajala (Magami Open Sciences Initiative), Zainab Ashimiyu-Abdusalam (Magami Open Sciences Initiative), Comfort Adesina (Magami Open Sciences Initiative)

Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utility

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The study introduces Malaria-Instruct, a curated instruction-following dataset for malaria virtual screening, and evaluates five open-source large language models (Gemma-2, TxGemma, and LlaSMol-Mistral) against classical machine learning baselines and proprietary models. Fine‑tuned LLMs outperform all baselines, with TxGemma-9B achieving the highest ROC‑AUC (0.731 ± 0.005) and LlaSMol-Mistral-7B delivering the best enrichment factor (EF@1% ≈ 4.99). The results demonstrate that domain‑specific fine‑tuning and chemistry‑aware pretraining are essential for reliable discrimination, positioning fine‑tuned open‑source LLMs as a resource‑efficient alternative for antimalarial virtual screening.

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