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. In this review, Kelly Chibale, John Woodland, Kathryn J. Wicht and colleagues discuss how functional genomics, chemical biology, artificial intelligence, and emerging therapeutic modalities are transforming antimalarial drug discovery and outline the scientific and collaborative strategies needed to deliver the next generation of accessible malaria therapies.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9