Abstract Despite decades of intensive research and substantial clinical gains, malaria remains a major global health challenge exacerbated by the continued emergence of drug-resistant strains of the causative agent, Plasmodium parasites. Encouragingly, recent advances in functional genomics, chemical biology and computational science are reshaping antimalarial drug discovery. In this review, we examine the discovery and development of next-generation antimalarials, including advances in phenotypic and target-based screening, omics-enabled target discovery and emerging therapeutic modalities such as long-acting agents, targeted covalent inhibitors and host-directed therapies. We further evaluate the opportunities and limitations of drug repurposing and discuss how artificial intelligence and data-driven approaches are reshaping target identification, compound optimisation and clinical development. Finally, we argue that future success will depend not only on scientific innovation but also on interdisciplinary collaboration, equitable partnerships, open science and the development of accessible therapies tailored to malaria-endemic populations.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6