10 papers · ranked by Valyu relevance
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Jose L Figueroa, Richard Allen White
We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC)…
Lin Chen, Yuhan Chen, Ziqi Cheng, Jing Guo + 20 more
MHPC512 is a massively parallel, special-purpose supercomputer designed primarily for atomic-level molecular dynamics (MD) simulations of biomolecular systems. It comprises 512 processor units interconnected by a high-speed three-dimensional torus network and employs a custom chip architecture that uses 35-bit…
Sumesh Kumar, Joseph Zambreno, Ashfaq Khokhar, Shoaib Akram + 1 more
Improving the speed and efficiency of database search algorithms that deduce peptides from mass spectrometry (MS) data has been an active area of research for more than three decades. The significance of the need for faster database search methods has rapidly increased due to the growing interest in studying non-model…
Nhan Ly-Trong, Samuel Martin, Nick Goldman, Nicola De Maio + 1 more
Phylogenetic analysis is essential to genomic epidemiology, for example in tracing the origin and evolution of SARS-CoV-2 variants during the COVID-19 pandemic. We previously introduced CMAPLE, a single-threaded implementation of the MAPLE algorithm designed for large-scale epidemiological genomic datasets. CMAPLE can…
Chun Gong, Qi Yang, Ruiwen Wan, Shengkang Li + 2 more
Joint variant calling is a crucial step in population-scale sequencing analysis. While population-scale sequencing is a powerful tool for genetic studies, achieving fast and accurate joint variant calling on large cohorts remains computationally challenging. To meet this challenge, we developed Distributed Population…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Authors not listed
Early on in the emergence of virtual high-throughput screening (VHTS), it was recognized that for validation to be robust and reliable, decoys should match actives as closely as possible in as many aspects as possible. This has given rise to several generations of validation sets that address previously reported…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
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Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…