Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform Discovery Consortium's pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay end points, using previously undisclosed comprehensive experimental drug discovery data sets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top-performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.
Mother Tongue Biblical Hermeneutics (MTBH) has become an influential interpretive method in Ghana, fostering wider availability and reception of mother-tongue Bibles and integrating indigenous languages into ecclesial discourse. By prioritising African socio-cultural, religious, and linguistic worldviews in the interpretive process, this approach has generated three distinct strands, each with unique emphases. Employing a narrative research method, this study critically examines these strands as developed at the Trinity Theological Seminary, the Akrofi-Christaller Institute of Theology, Mission and Culture, and the Department of Religious Studies at the Kwame Nkrumah University of Science and Technology. The study identifies the specific strand of MTBH promoted by each institution and explores how these can be integrated into a unified framework. It argues that harmonising the three strands will strengthen MTBH as a more coherent, resilient, and effective approach to biblical interpretation in the Ghanaian context.
In recent years, wireless technology is in demand. The Split Ring Resonator(SRR) based antenna has been designed, analysed, developed and fabricated. The researchers are doing research in designing technology-based, multiple application-oriented, efficient, and cost-effective RF structures. Incorporating all requirements of existing industries, various wireless applications-oriented, negative refractive index-based antenna structure is designed and analysed in this paper. The claimed antenna resonates at 1.70 GHz and 2.27 GHz frequencies. The output parameters show very good potential for the L and C band frequency applications.
Maximum power point tracking (MPPT) is a technique employed for with variable-power sources, such as solar, wind, and ocean, to maximize energy extraction under all conditions. The commonly used perturb and observe (P&O) and incremental conductance (INC) methods have advantages such as ease of implementation, but they also have the challenge of selecting the most optimized perturbation step or...
