The field of molecular property prediction and drug discovery is rapidly advancing, with a focus on developing innovative and interpretable models that can support safer drug therapies and clinical decision-making. Recent developments have seen the integration of machine learning and domain knowledge to improve prediction accuracy and generalizability. Notably, the incorporation of chemical reasoning and multimodal interaction techniques has led to significant performance improvements in molecular property prediction tasks. Additionally, the development of novel frameworks and models, such as those utilizing metaheuristic optimization and molecule-aware low-rank adaptation, has enhanced the ability to predict drug-target interactions and optimize molecular structures.

Noteworthy papers in this area include: A Hybrid Computational Intelligence Framework with Metaheuristic Optimization for Drug-Drug Interaction Prediction, which proposes an efficient framework for DDI prediction using a combination of molecular embeddings and a rule-based clinical score. MPPReasoner, a multimodal large language model that incorporates chemical reasoning for molecular property prediction, demonstrating significant performance improvements and exceptional cross-task generalization. Matcha, a novel molecular docking pipeline that combines multi-stage flow matching with learned scoring and physical validity filtering, achieving superior performance and physical plausibility in protein-ligand binding pose prediction.